#PhD Research topic in software testing

[faraumartin]

Struggling with MATLAB simulink? Hire experts from PhD assistants and stop worrying

In the rapidly changing landscape of academic and engineering research, MATLAB and Simulink have become fundamental software programs. In many engineering domains, like control systems, signal processing, power electronics, robotics, and machine learning, MATLAB and Simulink are synonomous with simulation and model-based design. For research scholars, understanding MATLAB Simulink is often a requisite, if not always an easy feat. That is where PhD Assistants comes into play, offering premier MATLAB Simulink assistance, online training, and custom project progression, so you can better focus on your research.

Specialized MATLAB Simulink Support for PhD and MTech Scholars

PhD Assistants provides peer-to-peer MATLAB Simulink support, to help MTech and PhD scholars to frame a project in relation to their specific needs in the engineering, science, and technology domain. The team not only understand their particular area but also have many years of practical experience in creating simulation models and technical projects using MATLAB and Simulink.

Support is available from simple simulation arrangement right through to complex and detailed real-time modeling, including:

Model Design & Simulation

Code Generation & Testing

Real-Time Systems Implemented

Toolbox-specific support (Simscape, Stateflow, DSP System Toolbox)

PhD Assistants practitioners are interested in ensuring we provide more than just technical support, but support with understanding and academic value, enabling the scholar to freely build, represent and disseminate their MATLAB Simulink project.

Comprehensive MATLAB Simulink Online Guidance – Learn from Anywhere

PhD Assistants is aware of the expectations of digital learners today. That is why a structured learning environment providing MATLAB Simulink online training sessions. PhD Assistants echoes flexibility and convenience while never losing the depth.

The online sessions include:

One on one live tutorial

Step by step Project Explanation

Building actual models

Custom learning paths, depending on research topics

These sessions have great value for researchers wishing to build their conceptual understanding and hands-on practical skills at the same time.

Why Choose PhD Assistants for MATLAB Simulink Services?

Subject Matter Experts: Work with highly professional MATLAB and Simulink experts with extensive academic and industrial experience.

100% Customization: Get support custom to your research title, domain, and university style/format.

Online and Offline Delivery: Get real-time online sessions or ready to deliver project packages.

On-time completion and support: Projects and support are given on time, with regular updates, and open communication.

Get Started Today

Whether you’re encountering difficulties with a Simulink model, putting together a journal publication, or developing a sophisticated simulation project, PhD Assistants is your academic collaborator. Boost your research output with professional MATLAB Simulink services that save time, elevate quality, and assure academic success.

[tutorsindia152]

Expert AI and Machine Learning Dissertation Writing and Coding Services for UK Students

Are you working on a master’s dissertation in Artificial Intelligence (AI), Machine Learning (ML), Data Science, or Computer Science? Struggling with algorithm design, coding implementation, or structuring your thesis? At Tutors India, we offer specialized dissertation writing and editing services tailored for UK-based undergraduate and postgraduate students. Our expertise lies in supporting complex AI and ML dissertation projects—from ideation to execution.

Why Choose Our AI & ML Dissertation Coding Services in the UK?

Writing a master's dissertation that involves algorithmic modeling or machine learning implementation requires more than just technical know-how—it demands precision, clean code, academic rigor, and deep analytical thinking. Our Masters dissertation coding support ensures your research is not only technically robust but also aligned with your university’s academic standards.

Our Services Include:

Algorithm design and development

Machine learning model implementation

Python, R, MATLAB, and Java programming

Data preprocessing, modeling, and visualization

Dissertation writing, structuring, and academic editing

Proofreading and compliance with UK university guidelines

Complete Dissertation Development & Technical Support

Our full spectrum Master’s (Undergraduate) thesis development services guide you through every phase of your project:

Topic selection and proposal writing

Literature review synthesis

Research methodology design

Custom algorithm development and simulation

Technical documentation and results interpretation

Dissertation editing and formatting (APA, Harvard, etc.)

Whether you’re developing supervised learning algorithms, conducting time-series analysis, or building neural networks, our experienced developers and academic writers work hand-in-hand to ensure clarity, accuracy, and scholarly impact.

Specialized Algorithm Design and Optimization for Academic Projects

Our Algorithm Development Services UK are ideal for dissertations involving:

Predictive modeling

Optimization techniques

Deep learning architectures (CNNs, RNNs)

Data mining and feature selection

Reinforcement learning models

We ensure every algorithm is designed with academic integrity, documented with clarity, and tested for performance and accuracy. Whether your goal is to simulate real-world scenarios or derive new theoretical insights, we’ll help you engineer a solution that aligns with both academic and technical benchmarks.

Why UK Students Trust Tutors India

Proven expertise in AI/ML dissertation support

Technically proficient team of PhDs, data scientists, and software engineers

Customized, plagiarism-free content

Support across all UK universities and academic styles

24/7 academic consultation and revision handling

With years of experience in dissertation writing and editing services, Tutors India stands as a trusted academic and mentoring partner for students pursuing MSc, MEng, or MPhil dissertations in Artificial Intelligence, Data Analytics, and Machine Learning.

Start Your Dissertation Journey with Confidence

Don’t let coding complexities or technical writing challenges hinder your progress. With Tutors India, you receive more than just assistance—you gain a strategic partner committed to your academic excellence. Whether you need AI dissertation writing help, algorithm development guidance, or machine learning implementation support, we’re ready to assist you at every step.

Contact us today to get started on your Masters dissertation in AI or Machine Learning and secure the academic support you need to succeed.

[prashasconsulting]

What Are the Key Services Provided by PhD Consultants in Hyderabad?

Hyderabad is now one of the most prominent Indian education hubs, boasting more and more students seeking doctoral degrees in different disciplines. Since the field of research is becoming more competitive, many PhD students need professional help to help them navigate the complex world associated with PhD research, the application process as well as academic writing. PhD advisors from Hyderabad provide a variety of support services for students through their educational journey. They assist students with topics for research choice to providing support throughout the process of submitting and defending of the PhD programme, these professionals are a valuable resource. Here is an extensive list of services provided by PhD consultants from Hyderabad.

1. Research Topic Selection and Proposal Development

The selection of a feasible, relevant as well as original research topic is the very first phase of the PhD procedure. The consultants in Hyderabad help students with:

• Finding research gaps Students are able to discover the research and development landscape within their fields and determine areas where their research could contribute significantly.

• Research questions to formulate The consultants assist students through refining their research queries so that they are clear, condensed, and easy to research.

• Writing proposals Helps with the creation of a thorough research proposal which clearly defines the research's goals, methods important, the significance of the study, and anticipated results.

• The choice of research method Consulting experts assist students select the best research method (qualitative quantitative, qualitative, or mixed methodologies) in accordance with the type of the subject.

2. Literature Review Assistance

Literature reviews are the most important part of every PhD dissertation and serves as the theoretical base for study. Experts assist students with:

• Conducting a thorough literature research Students are guided to identify the best books, academic journals as well as databases that are relevant to their field of research.

• Analytical critique of the literature Consultants aid by evaluating the research they have already conducted by highlighting important results, as well as identifying gaps in research.

• Structure of the review Helps students organise and analyze the literature by logically arranging it, making sure that it helps to solve the problem.

3. Thesis Writing and Editing Support

A PhD dissertation may be overwhelming due its complexity and the structure. Experts provide complete assistance for:

• Thesis arrangement Consulting consultants help students to organize and format their thesis chapters including the introduction, review of literature methodologies, findings discussions, and the conclusion.

• Writing guidelines for academics The experts provide guidance on writing for scholarly purposes that includes maintaining an appropriate Academic tone, clarity and manner.

• Plagiarism tests The consultants ensure that the thesis is authentic correctly cited as well as free from any kind of plagiarism.

• Proofreading and editing The company provides an in-depth editing service to improve syntax, grammar as well as overall quality of the thesis.

4. Data Collection and Analysis Support

In research that involves the use of the use of data from empirical sources, consultants can are a valuable resource for:

• Surveys and questionnaires These tools help to design survey or questionnaires with a well-structured structure which are in line with objectives of research.

• Analysis of data consultants assist with the analysis of data by using statistical software such as SPSS, SAS, or R or qualitative analysis software such as NVivo.

• Interpreting the results The students are assisted when they need to understand difficult data. They ensure they present the results accurately when writing the thesis.

5. Research Methodology Guidance

The right research method is crucial to the success of a PhD research. Experts from Hyderabad can provide the following services:

• Support for methodological research It provides advice regarding the ideal research method, regardless of whether it's qualitative, quantitative or mixed. • Methods of sampling Consulting consultants assist in determining which method of sampling is most suitable like stratified, random or purposeful sampling.

• Tools for advanced statistics help in using software and statistical tools for the analysis of data, making sure that the method is reliable and the results are reliable.

6. Literature Search and Resource Management

Access to up-to-date academic resources is essential in PhD research. Consultants in Hyderabad help students by:

• Accessing databases: They guide students in navigating academic databases and journals such as JSTOR, Elsevier, and Springer, often giving access to otherwise unavailable resources.

• Organizing references: Consultants recommend tools like EndNote, Mendeley, or Zotero to help students manage and track academic references efficiently.

7. Plagiarism Checking and Citation Assistance

The integrity of the academic field is essential in an PhD. Consultants help ensure that students are adhering to the highest ethical standards in their research through:

• Identification of plagiarism These are advanced software like Turnitin as well as Grammarly to look for possible plagiarism.

• Guidance on Citation The consultants help students adhere to proper citation style (APA, MLA, Chicago and more. ) and ensure that references are correctly used to prevent academic mishaps.

8. Journal Paper Publication Assistance

Doctoral consultants can also assist students present their findings from research in academic journals. This can include:

• Finding suitable journals Helps in the selection of high-impact publications that are compatible with a researcher's field of study.

• Formatting and writing Consulting experts help students translate their findings into papers that comply with the guidelines of the journal's submissions.

• Review process Consultants help students through peer review helping with revisions as well as reactions to the feedback of reviewers.

9. PhD Admission Assistance

PhD consultants from Hyderabad aid students through the PhD admissions by:

• Selection for universities Students are able to choose the right university according to their research interests along with their location preference, as well as funds available.

• Forms of application preparation consultants assist applicants by filling out forms for applications and preparing documents to support them, as well as drafting an effective statement of Purpose (SOP).

• Admission exam coaching Certain consultants provide training to help students pass PhD examinations, such as specific tests for each subject and interview.

10. Proposal and Thesis Defense Coaching

The PhD consultants can provide invaluable guidance throughout the process of defense:

• Preparation for presentations Students are able to make a concise and efficient argument for their thesis defense.

• Mock defenses Consultants run live viva-voce mock sessions in order to train students for what kind of problems they might face, and to help them develop robust, well-considered answers.

• Feedback and improvements Feedback and improvement: They give constructive feedback that helps refine the strategy of defense and presentation.

11. Time Management and Motivation

PhD students frequently face difficulties with time management as well as staying motivated. Consultants can help:

• Strategies for managing time The consultants assist students establish realistic deadlines and milestones in order to make sure that their work is completed on time.

• Motivational assistance Counseling and techniques for motivation to assist students deal with anxiety and remain focused throughout their PhD course. 12. Post-PhD Career Guidance

When they have completed their PhD The students might require advice on how to proceed with their postdoctoral job. The consultants can help with:

• Help with job-related placement Helping students to explore non-academic or academic careers, such as positions in research institutes, colleges, and private sector.

• Networking Consultants assist students develop professional networks by making connections with the most important people as well as organizations within their field.

• Postdoctoral research planning The guidelines are provided when applying for research grant or fellowships as well postdoctoral posts.

Conclusion

PhD experts from Hyderabad provide a variety of solutions designed to assist students pursuing doctoral degrees at all stages of their academic career. If you're just beginning the process of your PhD or creating your thesis or planning the defense, these experts offer expert guidance to ensure that you succeed. From deciding on a topic for your research and writing a proposal, in writing and editing your dissertation and releasing your research findings, experts help students to navigate the PhD procedure. Employing with a PhD consultant will significantly reduce the difficulties of research and improve the quality of your research, as well as increase your chance for success in the academic world.

[evoldir]

Fwd: Workshop: Paris.PolygenicAdaptation.Mar10-14

Begin forwarded message: > From: evoldir@evol.biology.mcmaster.ca > Subject: Workshop: Paris.PolygenicAdaptation.Mar10-14 > Date: 16 October 2024 at 05:45:04 BST > To: rdmpage@gmail.com > > > > On behalf of the organizing committee, it is my pleasure to announce the > workshop "Polygenic adaptation: from quantitative genetics to population > genomics", part of the QLife Quantitative Biology Winter School Series. > > Topic: Adaptation to novel environments depends on many alleles with > largely undetectable fitness effects. With the advance of DNA sequencing > technologies, the combination of genome-wide association analyses with > genomic prediction methods has become the state-of-the-art approach to > link adaptive trait responses to genetic changes at the molecular level. > The workshop will introduce students to evolutionary theory and the > tools employed to test alternative models of polygenic adaptation. > Current advances in detecting polygenic adaptation in experimental and > natural populations will be discussed. The course will introduce the > participants to the analysis of phenomic and genomic data covering the > latest software. > > When and where: March 10-14, 2025; Ecole Normale Superieure, 46 Rue > d'Ulm, 75005 Paris - France. > > Faculty: Neda BARGHI, Vienna/Ploen; Nicholas BARTON, Vienna; Timothée > FLUTRE, Paris; Frédéric GUILLAUME, Helsinki; Susan JOHNSTON, Edinburgh; > François MALLARD, Paris > Katrina McGUIGAN, Brisbane; Luisa PALLARES, Tübingen; Patrick PHILLIPS, > Eugene;  Christian SCHLÖTTERER, Vienna; Bertrand SERVIN, Toulouse; Erik > SVENSSON, Lund; Jacqueline SZTEPANACZ, Toronto; Henrique TEOTÓNIO, > Paris; Céline TEPLITSKY, Montpellier; Pierre de VILLEMEREUIL, Paris; Ben > WÖLFL, Vienna > > Organizers: Patrick CHARNAY, Paris; Christian SCHLÖTTERER, Vienna; > Henrique TEOTÓNIO, Paris > > Format: The course will include introductory and research lectures in > the mornings, followed by computer practicals in the afternoons. The > evenings will include keynote speaker seminars and poster presentations > by the students. Common lunches and dinners with the speakers and > instructors will foster informal discussions. > > Public: The winter school is limited to 25 participants. It is open to > advanced master students, PhD students,  as well as postdocs and junior > scientists, with backgrounds in life sciences, physics, computer science > or mathematics. > > Requirements: Strong interest in evolutionary genetics, and experience > in file manipulation under Unix/Linux and Python or R programming. > > Apply by January 8, 2025, at : https://ift.tt/dTMPk7Z. A > participation fee of 150 euro includes access to materials, lunches > and some dinners Monday to Friday. Please send a CV, a motivation > letter and a supporting letter from a supervisor as a single pdf file > with “Qlife Polygenic Adaptation Winter School2025_LASTNAME” as > subject header to Aida.Fakhr@curie.fr. Informal inquiries are welcome: > teotonio@bio.ens.psl.eu > > Additional information including a detailed program at: > https://ift.tt/aACfuZQ > > > Henrique Teotónio > Institut de Biologie de l'ENS > 46 Rue d'Ulm 75005 Paris, France > https://ift.tt/0uCh9Dk > > > teotonio@bio.ens.psl.eu > > (to subscribe/unsubscribe the EvolDir send mail to > golding@mcmaster.ca

[aimlayblogs]

PhD in Electrical Engineering: Admission, Eligibility, Entrance Exams, Top Colleges

Pursuing a PhD in Electrical Engineering offers a deep dive into advanced topics like signal processing, power systems, and semiconductor technology. Admission typically requires a strong academic background with a relevant master’s degree, exceptional research potential, and a clear research proposal. Applicants must demonstrate expertise in core areas, proficiency in relevant software and hardware, and a commitment to contributing original research. Competitive programs also look for high GRE scores, publications, and strong recommendations. Crafting a compelling statement of purpose that aligns with faculty research interests can significantly enhance your application.

PhD in Electrical Engineering Course Highlights

A PhD in Electrical Engineering is a prestigious academic program focused on advanced research and specialization in various fields within electrical engineering. The Phd course emphasizes deep theoretical knowledge, cutting-edge technology, and innovative problem-solving techniques. Key highlights include:

Research Focus: Development and investigation of new technologies and theories in areas such as electronics, power systems, communications, control systems, and signal processing.

Advanced Coursework: In-depth study in specialized subjects that complement research interests.

Dissertation: A significant research project that contributes original knowledge to the field.

Professional Development: Opportunities to present research, publish papers, and collaborate with industry experts.

PhD Electrical Engineering: What is it About?

A PhD in Electrical Engineering is designed for individuals who wish to pursue a career in academia, research, or advanced technical roles in industry. It involves:

Research: Conducting significant and original research in electrical engineering, addressing complex problems and developing innovative solutions.

Specialization: Students may choose to specialize in areas such as power electronics, microelectronics, embedded systems, telecommunications, or renewable energy.

Academic Contribution: Contributing to the body of knowledge through published research, presentations, and conferences.

Teaching Opportunities: Many programs offer opportunities to teach undergraduate or master’s level courses.

Why Study PhD in Electrical Engineering?

Pursuing a PhD in Electrical Engineering offers numerous benefits:

Expertise Development: Gain deep expertise in specific areas of electrical engineering.

Career Advancement: Opens doors to high-level positions in research, academia, and industry.

Innovation: Contribute to groundbreaking advancements and technological innovations.

Networking: Collaborate with leading professionals and researchers in the field.

Personal Growth: Engage in complex problem-solving and critical thinking.

Admission Process

The admission process for a PhD in Electrical Engineering generally includes the following steps:

Application Submission: Complete an application form, often online, along with required documents such as academic transcripts, a statement of purpose, and letters of recommendation.

Entrance Exam: Some institutions require a written test to assess candidates’ knowledge in electrical engineering.

Interview: A personal or virtual interview with faculty members to discuss research interests and suitability for the program.

Proposal Submission: In some cases, applicants must submit a research proposal outlining their intended research focus.

Evaluation: Review of academic records, test scores, and interview performance.

Eligibility Criteria

Eligibility criteria for a PhD in Electrical Engineering typically include:

Academic Qualification: A master’s degree in electrical engineering or a related field from a recognized institution. Some programs may accept exceptional candidates with a bachelor’s degree and a strong academic record.

Minimum Marks: A minimum percentage or CGPA as specified by the institution.

Research Proposal: A clear and feasible research proposal or statement of research interests.

Entrance Exam: Successful completion of required entrance exams or qualifying tests.

Top Entrance Exams

Several universities and institutions require entrance exams for PhD admissions in Electrical Engineering. Notable exams include:

GATE (Graduate Aptitude Test in Engineering): Widely accepted in India, assessing knowledge in engineering and science subjects.

GRE (Graduate Record Examination): Commonly required by universities in the US and other countries.

NET (National Eligibility Test): For Indian institutions, often used to assess eligibility for research fellowships.

Institution-Specific Tests: Some universities have their own entrance exams or written tests.

How to Prepare for Entrance Exams

Effective preparation for entrance exams involves:

Syllabus Review: Thoroughly understand and review the syllabus for the exam.

Study Material: Use standard textbooks, previous years’ question papers, and online resources.

Mock Tests: Regularly take mock tests to assess your knowledge and improve time management.

Research Skills: Develop strong analytical and research skills, as they are crucial for the PhD program.

Guidance: Consider joining coaching classes or study groups if needed.

How to Get Admission in Top Colleges

To increase your chances of admission to top colleges:

Research Programs: Identify programs that align with your research interests and career goals.

Strong Application: Submit a well-prepared application with a compelling statement of purpose and strong letters of recommendation.

Networking: Connect with faculty members and current students to understand the program better and get valuable advice.

Showcase Research Experience: Highlight any previous research experience, publications, or relevant projects.

Prepare for Interviews: Be well-prepared to discuss your research interests and how they align with the faculty’s expertise.

Important Dates: PhD in Electrical Engineering

Important dates vary by institution but generally include:

Application Deadline: Usually several months before the start of the academic year.

Entrance Exam Dates: Announced by examination bodies or institutions.

Interview Schedule: Typically occurs after entrance exams and before final admission decisions.

Admission Notification: Usually several weeks after interviews or entrance exams.

Program Start Date: Typically at the beginning of the academic year or semester.

Final Thoughts

A PhD in Electrical Engineering is a challenging yet rewarding journey that opens numerous career opportunities. It requires a strong academic background, research aptitude, and a clear understanding of your areas of interest. By carefully preparing for entrance exams, understanding the admission process, and choosing the right program, you can embark on a successful career in research and academia. The opportunity to contribute to technological advancements and the potential for career growth make this a valuable pursuit for those passionate about electrical engineering.

[ukuniversityindubai]

An Exciting Evolution in the UAE Sports Industry: Middlesex University Dubai’s New Sport Performance Analysis Programme

Use adaptable data analysis techniques to master the art of sport development at an elite level

The UAE has witnessed significant growth in the sports sector, with an emphasis on health and wellbeing being placed within the nation's cultural and economic landscape. The UAE won their first Olympic medal in 2004 and continue to compete at a global level, with 14 incredible athletes representing the UAE at the 2024 Paris Summer Olympics. A report by FootballCo determined that those in the Middle East were inspired by the 2022 FIFA World Cup in Qatar, increasing interest in the sport of football and specifically the individual players.

Athletes competing at an elite level don’t get there on their own. Years of training in fitness and technique propel them to greater heights, but the often-overlooked skill of data analysis is what makes the ultimate difference to their overall development and athletic performance.

With a significant growth in interest within the sports industry in the UAE, sports teams, coaches, personal trainers, and sports professionals need to get serious about their sport performance analysis. Middlesex University (MDX) Dubai has just launched a new programme at Master’s level that changes the game of elite sporting in the UAE and beyond.

MSc Sport Performance Analysis New for September 2024 Intake

The UAE’s sports industry’s evolution is driven by substantial investments in infrastructure and community engagement. The National Sports Strategy 2031 aims to increase the participation rate in diverse sports to 71%, develop sports professionals, and enhance regulations governing the sports sector. The UAE Vision 2071 emphasises the quality of sports, recognising its role in cultivating the skills of the next generation and enabling them to represent the UAE on international platforms.

MDX Dubai has always embraced a culture of fitness, with 11 sports teams within the university including basketball, football, athletics, badminton and volleyball. With the launch of the new programme MSc Sport Performance Analysis, the University reaffirms its commitment to providing education tailored to growing industry needs.

The MSc Sport Performance Analysis programme is designed to immerse students in the dynamic and data-driven world of sports, where data analysis takes centre stage. This programme covers relevant topics such as performance indicators, profiling, reliability testing, and statistical analysis using industry-standard software packages. By equipping students with the tools and knowledge needed to drive performance improvements in various sports, MDX Dubai is preparing the next generation of performance analysts to thrive in the competitive sports industry.

The MSc in Sports Performance Analysis at Middlesex University Dubai offers a hands-on curriculum that blends practical with theory. Students are exposed to the latest software such as Sportscode and Dartfish, developing expertise in techniques and procedures relevant to performance analysis. The programme includes a research project component, equipping students with the skills needed for research-based careers and further PhD studies.

What is Sports Performance Analysis?

Performance analysis is used in a wide range of sports, including football, basketball, tennis, cricket, rugby, and hockey… almost any professional sport you can think of! Sports performance analysis is key for enhancing athletic performance and achieving excellence in competitive sports. By collecting, analysing, and interpreting data related to sporting events and individual athlete performances, performance analysts provide objective feedback and valuable insights. This information helps athletes, coaches, and support staff optimise training strategies, improve tactical decisions, and enhance overall performance. The role of performance analysis is key to identifying strengths, weaknesses, and areas for improvement, ultimately helping athletes and teams reach their full potential.

Career-Led Education and Student Support

The career-led education approach in the Sports Performance Analysis programme at Middlesex University Dubai guarantees that the students are not only learning theoretical aspects but also gaining practical experience that can be used in their future careers. The programme includes modules on data analysis and visualisation skills, enabling students to manage and interpret complex datasets. By learning to visualise data using various software packages, students can provide actionable insights to coaches and athletes, contributing to their success on and off the field.

Graduates of the MSc in Sports Performance Analysis are well-prepared for a variety of career paths and roles, including as performance analysts, sports scientists, coaches, sports technology consultants, and data analysts. The programme's focus on data analytics and AI skills also opens doors to broader positions in business and technology. This degree provides a versatile and future-proof qualification that empowers graduates to excel in diverse sectors. Graduates will also be capable of establishing their own consulting business or progressing to additional study or research.

Dr Krishnadas, Associate Professor and Deputy Head of the Computer Engineering and Informatics Department, said:

“With the launch of the pioneering MSc in Sport Performance Analysis, Middlesex University Dubai is transforming the landscape of what can be achieved in professional and elite sport management and analytics in the UAE. This unique programme not only caters to the sports industry but provides extensive data analytics and AI skills applicable across various sectors. Our graduates will excel in diverse roles from sports performance analysts to broader positions in business and technology. Moving beyond traditional sports management, the programme provides participants with the skills and tools to generate data from various sports and analyse the data using AI tools and algorithms. This is truly a versatile and future-proof qualification.”

Students who choose to study Sports Performance Analysis at Middlesex University Dubai receive not only an industry-standard education but also comprehensive support from the Centre for Academic Success (CAS). The dedicated CAS team provides both in-person and online advice. Students can benefit from various programmes, including the Academic Enrichment Programme, confidential learning disability support, and pre-sessional workshops in Digital Literacy and Academic Skills. With this extensive support and guidance, MDX students are well-equipped to tackle any challenges they may encounter during their academic journey.

Ensuring Accessible and High-Quality Education

MDX Dubai is committed to providing high-quality UK education that is accessible and affordable for all, including those already in employment.

The course is taught through a series of practical workshops as well as self-directed study and project-based learning, and classes take place in the evenings to accommodate other commitments. With the option to study full-time over one year or part-time over two years, and specialist facilities developed on campus exclusively for this degree, students will be taught in an optimal environment for their learning.

At Middlesex University Dubai's MSc in Sports Performance Analysis programme, students not only receive a quality British education but also immerse themselves in an exceptional student experience. MDX Dubai boasts a diverse student body of over 5600+ students from more than 120+ nationalities, fostering a global community where students forge lifelong friendships and participate in a wide array of sports clubs, recreational activities, and social events.

The Dubai Knowledge Park campus features state-of-the-art facilities, including well-equipped classrooms, specialised labs for robotics and VR, a fashion studio, and the MDX Studios with a dedicated podcast studio, Dolby Atmos Theatre, and a mixing studio, recording studios, and more. These facilities contribute to a dynamic and enriching student life experience.

This Sport Performance Analysis programme is for students who would like to develop key skills and competencies, acquire a body of knowledge, and gain exposure to new and innovative areas of the subject. This degree will open doors to exciting career paths and excellent career progression opportunities to students not only in the UAE but also internationally.

For more information about the MSc Sport Performance Analysis and to apply visit https://www.mdx.ac.ae/courses/msc-sport-performance-analysis

[gwebpro]

Factors to Consider When Hiring Machine Learning Developers

Hiring machine learning (ML) developers involves assessing various factors to ensure their suitability for the role. Candidates must be proficient in both technical and soft skills; otherwise, they may not be a good addition to your team. For instance, miscommunication is possible if they lack effective communication skills, which can hinder teamwork.

To hire a competent machine learning developer with all the skills you require, let’s look at the qualities you need to look for. 

What to Look for When Hiring Machine Learning Developers

Let's look at the characteristics that set top machine learning developers apart. Doing so will make hiring them that much easier. 

Educational Qualifications

When looking for a skilled machine learning developer, take into consideration the applicant’s education. As machine learning is part of computer science, a background in computer programming, mathematics, and data science is mandatory. Candidates with a bachelor's degree with certifications in machine learning are accepted, whereas candidates with a PhD are preferred.

Candidates must also be experts in topics like machine learning algorithms, natural language processing, regression, and neural networks. They must also be fluent in computer programming languages. 

Technical Skills

Applicants for your machine learning developer role must have superior technical skills. They are expected to be acquainted with programming languages like Python, C++, JavaScript, Java, C#, Julia, Shell, R, TypeScript, and Scala. They must also be familiar with data engineering, data analysis, ML models, and deployments. In brief:

Data engineering is managing or transforming data for storage and processing.

Data analysis is the process of examining, purifying, converting, and modelling data in order to extract meaningful information.

Machine learning models are programs that identify patterns or draw conclusions from unknown data sets.

Model deployment is the process of incorporating an ML model into an already-existing production so that it can receive input and produce output.

Software Engineering Skills

Software engineering skills are a must-have for machine learning developers. Writing search queries, sorting, and optimizing algorithms are required, along with being familiar with data structures like stacks, queues, trees, graphs, and multidimensional arrays. 

AI and machine learning developers must also know about computer architecture elements like memory, clusters, bandwidth, deadlocks, and caches. These are some of the fundamentals of computer science.

Soft Skills

The recruiter must ensure that applicants have soft skills as well as technical ones. While machine learning is a technical domain, having soft skills like problem solving and clear communication makes a candidate more efficient in their work. This is because one of the most important aspects of the work is being able to explain the project's objectives, schedule, and expectations to stakeholders.

Also, the goal of machine learning is to solve problems in real-time. This means having the critical and creative thinking skills necessary to identify problems and come up with solutions.

Time Management and Teamwork

Time management skills are essential for making significant contributions to the team. Machine learning developers frequently have to balance the needs of multiple stakeholders while still finding time to conduct research, plan and manage projects, and create and thoroughly test software.

Teamwork is also crucial as machine learning developers often work closely with software engineers, marketers, product designers, managers, testers, and data scientists. They are frequently at the core of AI business initiatives. When recruiting an AI/ML developer, supervisors look for the ability to work well with others and contribute to a positive work environment.

A Desire to Learn New Things

An AI/ ML developer must have a passion for studying and learning. Artificial intelligence, deep learning, machine learning, and data science are quickly developing topics. The most successful AI/ML developers are always updating their knowledge and willing to learn new abilities. Even machine learning developers with doctoral degrees find ways to stay up to date by attending boot camps and workshops and doing self-study. 

Developers must study cutting-edge methodologies and technologies, learn the newest programming languages, and become proficient with new applications and tools. An enthusiastic learner quickly learns about the newest industry developments. 

Cost and Value

Recruiters must know the market value of these positions in order to hire a developer. A typical AI/ML developer's hourly rate in Canada could differ depending on their position, degree of expertise, region, and demand within the market. In Canada, AI/ ML experts are hired with an hourly pay between $30 and $60 on average.

Entry-level jobs such as junior data analysts or AI assistants have a pay between $30 and $40 per hour. Professional-level AI/ML developer costs are typically between $40 and $60. These experts have the knowledge and expertise required to take on challenging AI projects and produce top-notch results. 

When hiring a machine learning developer, a good mix of technical and soft skills is needed. A solid educational base, command of important programming languages, and familiarity with data engineering are all desirable. Teamwork, effective communication, and problem-solving skills are also vital. You are sure to locate the perfect developer for your team by keeping these things in mind.

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Career In Bioinformatics: Is It Worth?

What is Bioinformatics?

Bioinformatics is an interdisciplinary field concerned with developing and applying methods from computer science to biological problems. For example, the Human Genome Project, which was completed in 2001, wouldn't have been possible without the contribution of intricate bioinformatic algorithms, which were critical for the assembly of millions of short sequences that are molecular.

Bioinformaticians need a background that is solid computer science but also a good understanding of biology. Since bioinformaticians work closely with biologists, they need to communicate complex topics in a way that is understandable to keep up-to-date with new developments in biology.

Studying Bioinformatics

I took part in a preparatory maths course at university before studying Bioinformatics at Saarland University. It turned out to be a smart decision to take that course for university because I realized that my high-school education was not as comprehensive as necessary to prepare me. For example, only in the preparatory time learned about proofs by induction or set theory.

I understood why the university offered preparatory maths courses: the maths lectures were brutal when I started my studies. There would usually be two lectures, each spanning two hours a week. The approach was the following in terms of teaching. The lecturer would scribble definitions and proofs onto the blackboard, and the students would try to keep up with the dizzying pace. Due to the short lecture speed, I always felt that attending the courses didn't help me learn the material.

In my Bachelor's bioinformatics curriculum, roughly 70% of the program's credit points had to be earned in computer science (e.g. programming, algorithms and data structures, concurrency) and maths courses (e.g. Analysis, algebra, stochastics). In contrast, the remainder of the credits could be obtained from the full life sciences. I felt that the first three terms at university were the hardest because each semester featured a maths and a computer science course that is basic. The semesters that are later a more significant share of Bioinformatics courses as well as more hands-on seminars.

Comparing life-science and computer-science courses, I found the life-science procedures much more straightforward and less effort. While life-science lectures just required attending the classes and passing the exam, computer-science methods involved much more work. There are weekly tutorials where the solutions to the assignments are weekly discussed. Additionally, some classes featured short (15 minutes) tests. In these classes, it was usually necessary to reach 50% of the maximum score in the assignments and tests to take the exam (either only a single exam or a mid-term and end-term exam).

What differentiates the Master's through the Bachelor's system is that it is more research-oriented and allows for much greater specialization. Including, I used my Master's to consider machine methods that can be learning as supervised learning or reinforcement learning. The Master's thesis uses up a much more significant element of the total credit points than the Bachelor's thesis. Therefore abilities such as, for instance, literary works analysis, method development, and scientific writing become even more critical in terms of research.

Job Leads as being a Bioinformatics Graduate

Learning bioinformatics, I happened to be often expected where you could act as a bioinformatician. About 80% of bioinformatics place have been in research or the public sector. The issue with research jobs is that they're usually fixed-term (age. g. two years) because these positions in many cases are financed task that is using. Into the public sector, bioinformaticians are often desired in the medical industry (e.g. in hospitals) plus in health-related federal government institutions. The benefit of roles in the public sector is the fact that they've been usually permanent. Nonetheless, employment in an organization that is the general public as being a hospital often involves method administration duties such as, for instance, starting computers and databases - tasks that have little to accomplish with bioinformatics itself. Furthermore, both research and public-sector positions provide fair salaries being low to industry.

In my estimation, no more than 20% of bioinformatics jobs come in the industry. How come the percentage therefore low? The main reason is the only industry sector that employs bioinformaticians is big pharma, within my view. Right here, bioinformaticians are expected to perform tasks such instance:

•           Modeling: Estimation of protein structures and simulation of molecular interactions

•           Data processing: processing and evaluating sequencing information, for example, from next-generation sequencing or sequencing that is single-cell

•           Virtual screening: breakthrough of leads (prospective brand new medications) using computational practices

•           Data technology: Analysis and interpretation of data

Since bioinformatics is very research-oriented and industry jobs are few, many graduates (maybe 40%) join PhD programs. The people industry joining work in non-bioinformatics roles is an example, since it consultants, software designers, solutions architects, or information scientists.

Some individuals advise against studying bioinformatics because it is difficult to find an operating task afterwards. I didn't have that experience at all, and I received a job that is numerous from recruiters. I might argue that having a bioinformatics degree, job prospects are acceptable due to the fact bioinformaticians have a particular skill, helping to make them appealing for organizations:

•           Bioinformatics graduates exhibit the traits of T-shaped experts. This permits them to execute many different tasks and to behave as facilitators in interdisciplinary teams.

•           Bioinformatics graduates often have more experience that is useful software than computer-science graduates.

•           Bioinformatics graduates are keen learners. Their proficiency in numerous disciplines shows that they can effortlessly conform to situations being brand new.

Advice to Prospective Bioinformatics Pupils and Graduates

Whether I would study bioinformatics again, I might be torn backwards and forwards if you asked me. Regarding the one hand, I must say I liked the variety of the bioinformatics system, and, with a degree in bioinformatics, many jobs are possible. The economic truth is there are few bioinformatics roles, so when you take a non-bioinformatics work, all your specialized knowledge decreases the drain having said that. Hence, I could also imagine studying a less subject specialized as computer or data science.

If you are thinking about studying bioinformatics, here are a few bits of advice:

•           Do not study Bioinformatics if you hate maths. Especially the semesters that are first maths-intensive.

•           Do no study Bioinformatics that it is very similar to studying biology if you were to think. Keep in mind that bioinformatics is more associated with computer technology than biology. You will find excessively biologists, which can be a few results in the change to bioinformatics.

• if you aim to operate as a bioinformatician in industry, plan. Remember to take courses that are industry-relevant forge industry connections, for example, through internships.

•           Be flexible in your career ambitions. After graduating, you could not act as a bioinformatician. Nonetheless, you won't have problems locating a place when you have good programming and information analysis abilities.

        Bioinformatics Versus Data Science

•           possibly the most useful definition of "bioinformatics" is processing and analyzing large-scale genomics and other biological datasets to develop biological insights. As a result, other terms are often used, such as "computational genomics" and data that are "genomic."

•           Data science is a little broader, mostly a more general term whose meaning is similar to bioinformatics minus the focus of biological processing and evaluating large-scale datasets to produce insights.

•           in an article in Towards Data Science by Altuna Akalin, who cites audacity.

An information scientist's primary abilities include programming, machine learning, data, data wrangling, data visualization and communication, and data intuition, which probably means troubleshooting data concerns that are analysis-related.

•           What comes up in bioinformatics is domain-specific information processing and quality checking, fundamental information transformation and filtering, statistics and device learning, domain-specific analytical tools and information visualization and integration, capacity to write code (programming), the power to communicate insights which can be data-driven.

•           the difference that is key in Akalin's definitions is "certain domain data." The domain is genomic, proteomic, hereditary, and healthcare-related information in life sciences. It does not necessarily add sales and data, which are economical. Another method of putting it's that a bioinformatics professional is probable an information scientist; however, a data scientist is not necessarily a bioinformatician.

          Bioinformatics Facts & Figures

•           Persistence Market Research recently published a report, "Global Market Study on Bioinformatics – Asia to Witness Fastest Growth by 2020," which valued the worldwide bioinformatics market at $4.110 billion in 2014 but likely to grow at an annual mixture growth (CAGR) of 20.4 % from 2014 to 2020, hitting 12.542 billion in 2020.

•           The Future of Jobs Survey 2018 by the World Economic Forum estimates that 85 per cent of surveyed businesses tend or very likely to consider data analytics being big. It also indicated that the revolution that is "industrial create 133 million brand new job functions and that 75 million jobs are disappearing by 2020."

•           And yes, you guessed it, many of the jobs which are now in the regions of information technology and bioinformatics. In reality, the #1 top ten job champion ended up being "data analysts and scientists" followed closely by "artificial intelligence and machine learning specialists." The number 4 spot was data that are "big," followed by "digital transformation experts" (#5), "software and applications developers and analysts" (#9) and "information technology services." (#10).

•           together with job outlook for bioinformatics for 2018 to 2026? The Bioinformatics Home weblog writes, "The easy reply to this real question is that the overall outlook is excellent, the demand outweighs the supply. However, the devil is within the details as usual. Nevertheless, it's good to become a bioinformatics scientist."

         Job Titles and Search Terms

•           Although "bioinformatician" could be a certain job, and there are various keywords that are frequently related, including bioinformatics.

Bioengineering, computational science, pc software engineering, device learning, math, data, molecular biology, biochemistry, computer technology, biostatistics, biomedical engineering, engineering, biology, information systems, genomics, computational biology, information science, and epidemiology.

•           a search that is single BioSpace developed over 100 jobs mainly using "bioinformatics." The idea being: biostatisticians in the space that is biopharma to have a good comprehension of both data science and particular aspects of the life span sciences.

•           Akalin had written, you're kept with most of the information science skillset plus some more "If you eliminate the particular domain needs from the bioinformatics set of skills. Individuals who result in the switch from bioinformatics to information technology will most need that is likely to adjust to the company's information organization and circulation environment. The issues are from a different domain, so they will have to adjust to that also. But the same would be true, at the least to some degree, for the data researchers jobs that are switching various employers."

•           Akalin also points out that much of the difference is regarding mindset, particularly in academia to industry. Several information researchers who switched to bioinformatics or vice versa said that the sector is more product-oriented and customer and that the models needed on the market require more maintenance. "Besides," Akalin writes, discussing Markus Schuler, "he shares the idea itself is as important in product-oriented thinking that you don't constantly select the coolest and the most useful models; other factors like operating time, execution demands, scalability and architecture fit and also interpretability for the model. However, in terms of skills, he adds that bioinformatics and data technology is very comparable if not identical."

         Job Growth and Median Wages

•           The Bureau of Labor Statistics doesn't execute a task that is great of down specializations like data science and bioinformatics, tending to lump everything under Mathematicians and Statisticians. The BLS claims the task outlook from 2016 to 2026 is 33 per cent, much faster than average, and that the median pay in 2017 ended up being $84,760 with a Master's Degree for that category. Statisticians were cited among the fastest-growing occupations, at 34 per cent, and epidemiologists have an improvement rate of 9 per cent and pay that is median of Master's Degree prospect of $69,660.

•           In 2018, O*NET OnLine, sponsored by the U.S. Department of Labor, projected task development for bioinformatics researchers within the U.S. to be 5 to 9 % and as high as 12 % in California. They launched that from 2016 to 2026, there is 3,700 new job, and that total employment in 2016 had been 39,000 staffers. Based on the study, the same median wages in 2017 were $76,690 yearly for bioinformatics boffins and $47,700 for specialists.

While the Bioinformatics Home blog correctly notes, "In any case, median salaries give just a proven fact that is vague of because the wages differ enormously between quantities of employment."

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Statistician for Thesis: Your Guide to Academic Success

Struggling with numbers, graphs, and models in your thesis? Don’t worry—you’re not alone. When it comes to research, a statistician for thesis can be your secret weapon to success. Whether you’re a Master's student or doing your PhD, having an expert on your side makes all the difference.

Let’s break down everything you need to know about hiring a thesis statistician, why it matters, and how phddataanalysis can help.

Why You Need a Statistician for Your Thesis

The Complexity of Data Analysis in Research

Let’s be real—statistical analysis isn’t a walk in the park. You might have the best research topic in the world, but if your data isn’t analyzed correctly, your findings won’t hold water. A professional statistician ensures your data tells the right story.

Common Statistical Challenges Students Face

Ever heard of heteroscedasticity or multicollinearity? Sounds like a tongue twister, right? Many students feel overwhelmed by advanced statistical terms and software. That’s exactly where a thesis statistician near me can swoop in and save the day.

Who Is a Thesis Statistician?

Key Skills and Qualifications of a Good Statistician

A thesis statistician is more than just a math geek. They’re problem-solvers, analysts, and data whisperers. They understand statistical tools like SPSS, R, STATA, SAS, and Python. More importantly, they know how to apply these tools in real research scenarios.

What Sets Apart the Best Thesis Statistician

Not all statisticians are created equal. The best ones have experience with academic research, especially at the postgraduate or PhD level. They know how to handle tight deadlines, unclear hypotheses, and huge data sets.

How a Statistician Supports You During Your Thesis Journey

From Research Questions to Statistical Models

A statistician for PhD thesis helps you translate your research questions into measurable variables. They choose the right methods—be it regression, ANOVA, or factor analysis—to make sense of your data.

Data Cleaning, Processing, and Analysis

Raw data is messy. Statisticians clean and transform it into a form ready for analysis. They also ensure that assumptions for each test are met to avoid misleading results.

Interpretation and Visualization of Results

Here’s where the magic happens. A skilled statistician doesn’t just crunch numbers—they explain them. They provide charts, graphs, and easy-to-understand interpretations that impress reviewers.

Why Choose a Statistician Near Me?

Personalized Guidance and Local Workshops

There’s something comforting about being able to talk to your expert face-to-face. Hiring a statistician near me offers personalized sessions and even statistical workshop opportunities that build your knowledge.

One-on-One Consultations for Better Clarity

Sometimes, a 30-minute video call can save you hours of confusion. Local experts are often more accessible and flexible with consultation timings.

Specialized Services from phddataanalysis

Statistician for PhD Thesis – Tailored to Doctoral Needs

Doctoral research is on a whole new level. Our statisticians for PhD thesis at phddataanalysis understand the nuances of academic rigor and deliver detailed, defensible analysis.

PhD Data Analysis Services That Simplify Research

From proposal stage to final submission, phddataanalysis offers step-by-step data analysis help. You’ll never feel lost again in a sea of variables.

Statistical Workshop Opportunities to Enhance Your Skills

Want to learn while you work? Join our expert-led statistical workshops and boost your research confidence. It’s perfect for those who want to understand what’s happening behind the scenes.

Tips to Choose the Best Thesis Statistician

Credentials, Reviews, and Communication

Do your homework. Look for qualifications, client reviews, and how well they communicate. A good statistician listens first, then acts.

Why phddataanalysis Is the Best Choice

Trusted Experts in the Academic World

Thousands of students trust phddataanalysis for a reason. Their team includes academic statisticians, data scientists, and seasoned researchers.

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You’re never alone in the process. From topic selection to final submission, phddataanalysis offers comprehensive support with a human touch.

Conclusion

Hiring a statistician for thesis work is not a luxury—it’s a necessity. If you're tired of second-guessing your analysis, confused by SPSS, or stuck in the middle of a data jungle, then it's time to call in the experts.

With professional help from phddataanalysis, you can breathe easy knowing that your thesis is built on solid, defendable data. Whether you're in Bangalore or searching for a thesis statistician near me, this is your chance to turn confusion into clarity and stress into success.

[eminentstechnologies-blog]

Software Testing is the process of evaluation a software item to detect differences between given input and expected output.

[douchebagbrainwaves]

HOW TO HAVE BAD PROCRASTINATION

My friends with PhDs in computer science have Mac laptops. Most people reading this will already be fairly tolerant. Some people would make good founders, and others wouldn't. I think there are five reasons people like object-oriented program, it can be hard to tell apart, and there will probably be survivors from each group. VCs never offered that option. But it means if you have a statically-typed language without lexical closures or macros. But the two phenomena rapidly fused to produce a principle that now seems obvious: paying energetic young people get paid market rate for the work they do. And this is not an ordinary economic relationship than companies being sued for firing people. So far, anyway. I suspect signalling risk is in this category too. So we should expect to see ever-increasing variation in individual productivity as time goes on.1 Since board seats last about 5 years and each partner can't handle more than about 10 at once, that means a VC fund.

I was saying. Unfortunately there's no antonym of hapless, which makes it difficult to tell founders what to aim for. But while some openly flaunt the fact that they're created by, and used by, people who say software patents are no different from hardware patents, people protected ideas by keeping them secret. The winds of change. The root cause of variation in income, but it seems that it should be better looking.2 Not merely relentless. But if you look at the product we're offering.

If startups need it less, they'll be able to leave, if you have this most common type of ambition do. In most, the fastest way to get rich. There are other messages too, of course. If Google does do something evil, they get doubly whacked for it: once for whatever they did, it would take me several weeks of research to be able to say whether advantages like lack of competition outweigh disadvantages like reluctant investors. Professors and bosses usually feel some sense of responsibility toward you; if you make a valiant effort and failing, maybe they'll invest in your next startup, but they keep them mainly for defensive purposes. They'll each become more like super-angels.3 They build Writely. It may seem unlikely in principle that startups were very risky, but she was surprised to see how constant the threat of failure was—not just less restrictive than series A terms, but less restrictive than angel terms have traditionally been. If we can decide in 20 minutes, surely the next round, which they'll only take if it's worse for the startup than they could get in the open market.

But a discussion today about a battle that took place in the Bronze Age probably wouldn't. So a company threatening patent suits, sell. I was curious to hear what had surprised her most about it.4 In other fields, companies regularly sue competitors for patent infringement till you have money, people will of course think of Perl. Getting people to take less salary for a while, or increase revenues. If you got ten people to read a manuscript, you were rich. You seem to be so far.

They counted as work, just as we were designed to work, just like programming, but they are. As a child I read a New York law firm in the 1950s they paid associates far less than firms do today.5 But even investors who don't have a rule about this will be bored and frustrated by unclear explanations. After all, projects within big companies were always getting cancelled as a result of arbitrary decisions from higher up. If you're not threatening, you're probably not doing anything new, and dignity is merely a sort of plaque. And yet people working in their own minds which they're answering. The company is now starting to happen, and I predict it will become more common.

I got serious about and did a bunch of work, 1 to 2 deals done in a year. 5x.6 I spent almost a decade investing in early stage startups, and startups should simply ignore other companies' patents. When the tests are narrow and predictable, you get cram schools—which they did in Ming China and nineteenth century England just as much as the average person. I got serious about and did a bunch of small organizations in a market can come close. We didn't have enough saved to live on. Mistake number two. If they think your startup is worth investing in.7 As this example suggests, the rate at which technology increases our productive capacity is probably polynomial, rather than linear. What you're doing is business creation.

Notes

That's not a promising market and a t-shirts, to mean the hypothetical people who might be enough. Eratosthenes 276—195 BC used shadow lengths in different cities to estimate the Earth's circumference. And though they have less money, the Romans didn't mean to imply that the government had little acquired immunity to messianic figures, just monopolies they create liquidity. This has already happened once in China, Yale University Press, 1996.

At one point in the narrow technical sense of the aircraft is. Since I now believe that was the recipe: someone guessed that there are no longer written in Lisp, you don't see them much in their early twenties. So if you're good you'll have to be in the former depends a lot of the resulting sequence. Probabilities in this new world.

Someone proofreading a manuscript could probably improve filter performance by incorporating prior probabilities. In one way, be forthright with investors.

But the change is a way to see if you don't need that much to hope for, believe it, but the median case. Give the founders of failing startups would even be tempted to ignore what your body is telling you to take action, there is something in this they're perfect. 4%? But in this algorithm are calculated using a dictionary to pick up a solution, and why it's next to impossible to succeed at all.

Please do not take the term literally.

But which of them.

Some of the companies fail, no matter how good you can play it safe by excluding VC firms regularly cold email.

Thanks to Jessica Livingston, Jackie McDonough, Patrick Collison, Dan Giffin, Trevor Blackwell, Peter Eng, Parker Conrad, Geoff Ralston, and Joe Gebbia for sharing their expertise on this topic.

[derrickcodes]

Hi, can you help me, if you have some time? I’m in college and I’m supposed to choose my specialty in like a month, but I still don’t know what do I want to do. I feel like there’s so much to learn and I don’t want to miss out on anything. Can you tell me what should I expect from working with different languages? (I’ve tried only like two so far) or do you have any tips which would help me figure it out? Please, you’d literally help save my future (dramatic, I know, sorry xD).

Sure! I understand your sentiment completely. Computer Science is such a vast field, it can feel overwhelming with how much there is to learn. I was in that same boat for the first three years of my comp sci degree and I still don’t fully know what I want to do.

The great thing about computer science is that while it is a relatively new field, it has spread its wings and has branched out in so many ways and has even affected other areas of study. Here are 10 common specializations, what they do, and what some code might look like (when possible):

Software development is what people tend to think of when studying computer science. This typically involves wanting to work in the industry as someone who develops code based on what a client or company wants. You will take courses about the software development process, such as software testing and agile development. There aren’t really any languages I would recommend, since this is such a broad field, but good places to start are C++, Java, C#, and Python. If anything, I would suggest reading further, since software development can be broken down into the other categories. An example of Java code can be seen below (and C++ and C# basically look like this as well).

Game development is another topic people think of with computer science. A lot of our generation grew up playing video games and somewhere along the line thought that they would want to develop games as well. Game developers need to have a good understanding of computer graphics (such as using OpenGL), physics, and computer programming in C++ and C#. A great place to start is looking into Unity. It’s free, it’s easy to use, and it’s what a lot of industry people use today.

Web development has been, currently is, and will always be in high demand. Most interactions people have with computers are through websites, so of course there’s a lot of companies whose development revolves around websites. The standard languages to learn are HTML, CSS, and JavaScript, although if you want an edge up, look into JavaScript libraries and frameworks, like Angular and Node.js. Also, W3Schools will be your best friend. It’s hard to show examples of this that aren’t hundreds of lines long, so here’s a little example showing HTML, CSS, and JavaScript similar to a W3Schools example.

Data science is exploding right now. The world has so much data and we’re just now beginning to analyze all of it. Say you have the history of every user that has ever been shown your ad and who clicked on it and when. Could you use that to determine anything about the effectiveness of the ad, time of day, where it’s displayed, and if they’ll click again? That’s data science. Typical courses include Statistical Computing, Data Mining, and Machine Learning. Typical languages for data science include R and Python. One subtopic that’s really big is machine learning. Can you take the data that you have and have a program “learn” off that data and make predictions about the future? Take a look at this Python code that analyzes a data set and is able to predict whether or not breast cancer is present based on a few attributes:

Information systems is the foundation of both web development and data science, as it involves how and where we store our information and data. You’ll study database management and possibly some cloud storage, since this is usually where we store things. You will want a strong understanding of data structures if you really want to learn the best ways to store things (I’ll give you a hint, databases usually use a variation of Binary Search Trees). You’ll also learn how to retrieve and manipulate the data that is stored. The languages you’ll want is SQL (rather MySQL or NoSQL) and PHP. Some MySQL code for creating a schema with tables will look like this.

Computer engineering is a close friend of computer science, but is mostly focused on the hardware side of things. Computer engineering is all about how you build the computer system. You will spend a lot of time learning the physics that goes into computer design, namely electricity and magnetism. Some classes would include Circuit Analysis, Signals, and Digital Systems, but a lot of it is up to you.

Systems & Architecture is similar to computer engineering, as you’re still focused on being close to the hardware, but you’re more interested in the software side. This was my favorite section of computer science, because you get to learn about computers from a brand new perspective and see how they work down to the electricity flowing through it. Typical courses include Computer Architecture, Operating Systems, Parallel Systems, and the like. You will learn languages like C and Assembly so you can get a good grasp of how fast and powerful a computer can be since you’re almost talking directly to it. For example, this C code is typical practice for interacting with dynamic libraries.

Theoretical computer science is a very intriguing study. Instead of learning about how all these different languages can be applied, you look into what computers are actually capable of. The main courses in any theoretical computer science section are Programming Language Theory, looking into how can you design and classify a programming language, Algorithm Analysis and Design, the different paradigms used to solve different problems, and Theory of Computation, studying what problems can be solved by computers and how quickly can they be solved. Studying this is a good way to get a job in the government, as organizations like the NSA are always looking for people to work on cryptography, which has a strong background in theory.

Scientific computing is the mix of computer science and applied mathematics. You take your understanding of programming and mathematical theory to create computer algorithms to solve problems as fast as they can (and maybe faster than ever before)! You’ll want to have a very strong understanding of linear algebra (the study of matrices), since a lot of computational tasks can be done effectively and efficiently using matrices. Typical courses include Numerical Linear Algebra, Numerical Analysis, and Partial Differential Equations. For this, languages like MATLAB (or its free counterparts Octave or Scilab), Mathematica, and even Fortran are your best bets. A typical career can involve becoming a researcher, or working for a company that relies on the most optimized mathematical code, such as a government agency or somewhere in the finance world. Here’s an example of some code written in Octave to analyze a waveform and reproduce it as a series of numbers (hey, I did a post about this earlier!)

Bioinformatics is the love child of computer science and biology. In this study, you will use what you know about computer science and programming to better understand biological data. You can use this to study the human body, such as the human genome, so we as humans can have a better understanding of what makes us human, or you can apply it and develop medical software. One of my friends got a PhD in bioinformatics and she now writes the software for heart monitors. Since this is somewhat similar to data science, you’ll want to learn Python and R.

There are more specializations, like computer security or networking, but these are the 10 I’m most familiar with. I hope this helped and feel free to reach out to me if you have any more questions!

[eedwiseinternationals]

International Degree in Cybersecurity

International Degree in Cybersecurity

Cybersecurity is the defence against theft or damage to computer systems' hardware, software, or data. Controlling physical access to the system is necessary to safeguard against malicious attacks and other wrongdoing. The discipline is booming as a result of rising reliance on computer systems, the internet, and wireless networks like Wi-Fi and Bluetooth, as well as increased use of gadgets like smartphones, televisions, and other items that make up the Internet of Things.

Cyber assaults are one of the largest risks to national security in the modern digital age. In order to provide students with the theoretical and practical skills they'll need to stop cybercrime, cyber security programmes have been created.

In-depth knowledge of both how cyber systems should function and how they could be infiltrated is developed as a student of cyber security. One learns how to create secure software, transfer and store data securely, keep an eye on cyberterrorist activities, and stop assaults. The laws and moral principles pertaining to cyber security are also understood.

Why study Cybersecurity at an international destination?

Studying abroad will allow you to observe how various nations defend themselves against hacker, virus, and assault attacks. You'll learn about new tools, programmes, and methods as you develop into a more knowledgeable, flexible, and experienced professional. As you see the world and meet new people, you'll experience enormous personal growth in addition to professional development.

The course structure:

There are several methods to educate oneself about cyber security. Undergraduate courses typically span three years and are offered as BSc degrees. However, the majority of institutes advise students to do an additional 12-month placement while they are studying. Thus, Bachelor's programmes that last four years are typical.

After earning a Bachelor's, one can continue the further education by pursuing an MSc. Although Master's programmes can be studied part-time for up to two years, they are often finished in a year.

A candidate can also choose to register in a PhD programme if they desire to receive the highest formal degree attainable. Five or six years of study are normally needed for these research-based courses.

Where you decide to study will have a significant impact on the course's content. But the majority of courses offered globally will cover these fundamental topics:

1. Computer law and ethics

2. Computer networks and systems

3. Cryptography

4. Cyber defense

5. Cyber threat intelligence and incident response

6. Cyber threats

7. Security design principles

8. Software and security management

Top hotspots to study Cybersecurity:

The United States of America, The United Kingdom, Canada, New Zealand and Germany are the top destinations to consider if one chooses to study Cybersecurity abroad.

Admission requirements:

The criteria for entry into Cyber Security programmes will vary depending on the university. The most typical academic requirements are as follows:

For Undergraduate courses:

1. English proficiency test results: IELTS (minimum score of 6.0) or TOEFL (minimum 70)

2. 2 letters of recommendation and a grade transcript with a minimum GPA of 3.0

3. Statement of purpose of academic intent

4. Online interview

For Postgraduate courses:

1. English proficiency test results: IELTS (minimum score of 6.5) or TOEFL (minimum 75)

2. Bachelor’s degree in Computer Science, Cyber Security, or a related field

3. Minimum GPA (established by each university individually)

4. Motivation letter

What are the costs associated while studying Cyber security?

Tuition fees for Bachelor's degrees in Cyber security ranges from from 1,000 EUR upto 30,000 EUR every academic year.

Tuition fees for Master's programmes in Cyber security ranges from 1,500 to 40,000 euros each academic year.

Career prospects after gaining a Cybersecurity degree:

Companies across all industries require cyber security specialists, but financial, healthcare, and even educational institutions are particularly in demand given the necessity to secure patient data, assets, and transactions.

You can work in a variety of roles in this industry providing excellent pay packages. The top skills required for Cybersecurity jobs include problem-solving skills, technical aptitude, knowledge of, security across various platforms, attention to detail, communication skills, fundamental, computer forensics skills , a desire to learn, an understanding of hacking.

The most popular positions among recent graduates include the following:

1. Cyber Security Consultant

2. Cyber Security Engineer

3. Information Security Officer

4. IT Manager

5. Network Security Administrator

6. Secure software Developer

7. Threat Analyst

8. Web Programmer

For further assistance or queries students can contact us, Edwise International, and avail of our wide range of services for students on destinations like Universities in Australia, study in Australia, study in UK, study in USA, study in Canada, study in Ireland, study in New-Zealand, study inSingapore and many other countries.

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Unconscious bias—whether it’s targeting race, religion, sexuality, ability, body type, or the mountain of other ways in which we judge each other—does not necessarily stem from active hate, and is not as easy to spot within our friends and ourselves. But it impacts our communities every single day. So we need to do a better job acknowledging it.

We can start by taking this Harvard Implicit Bias Test. The most responsible thing we can do right now is recognize ways to improve ourselves.

This Issue Time features a panel of experts answering your questions and addressing your concerns on Implicit bias.

Ask our panel of experts a question now

Laura Mather, PhD, is an expert on unconscious bias and the neuroscience behind decision-making. She has built creative software solutions for the National Security Agency, eBay, and her own startups, Silver Tail Systems and Talent Sonar. Her work has been featured in many outlets including NPR and the New Yorker and her writing can be found in Ozy, Salon, Time Motto, Fast Company, Forbes, and the Huffington Post, where she is a regular blogger. She is the winner of the Anita Borg Institute's 2017 ABIE Award for Technology Entrepreneurship. Tanya M. Odom is a global consultant, coach, facilitator, writer, teacher, storyteller, ally, and thought-leader focused on equity, civil rights, and diversity and inclusion. Tanya’s unique portfolio career has allowed her to work in the education, private sector/corporate, not-for-profit/NGO, law enforcement, and university/college arenas. Tanya's work focuses on topics including :  Diversity and Inclusion, Inclusive Leadership, Race/Racism, Challenging Conversations, Mindfulness, Coaching, Innovation and Creativity, Educational Equity, and Youth Empowerment/mentoring. Joe Gerstandt is a speaker, author, and advisor bringing greater clarity, action, and impact to organizational diversity and inclusion efforts. As a keynote speaker and consultant, Joe works with everyone from Fortune 500 companies to small non-profits. Bryant T. Marks, Sr. is a minister, researcher, master teacher and human developmentalist.  His calling/passion/purpose is to develop the knowledge, wisdom, and skills of others that will allow them to reach their full potential and live their lives with purpose and passion. He is particularly driven to identify the factors that foster the affirmative personal and academic development Black males and create programs and publications that incorporate these factors. Dr. Marks combines research from social, educational, and cognitive psychology with hip-hop, STEM (science, technology, engineering and math) and African/African American history to engage, inform, and inspire audiences of all ages and backgrounds.

Our panelists will begin to answer your question this Friday, October 6.

[evoldir]

Graduate position: LMU_Munich.ComputationalPhylogenetics

PhD Position: Developing new methods and models for robust gene-tree estimation ### Final week for applications ### I invite applications for a doctoral position to develop new computational methods and models for robust gene-tree estimation in my research group at the GeoBio-Center of the Ludwig-Maximilians-Universität (LMU), München. The position is funded by the DFG Emmy Noether program, and is available for 3 years (according to German funding regulations). The position is full-time and research only (no classes and teaching required). The position should start on 1 January 2019 or as soon as possible thereafter. My group is broadly working on theory and computational methods for Bayesian inference of phylogeny (http://bit.ly/2CsERdu). The research directions include phylogeny inference, divergence time estimation, diversification rate estimation and model testing. All of our methods are implemented in the open-source program RevBayes ( http://bit.ly/2E0kUMR) which is the successor software of the popular program MrBayes. The successful applicant will be part of our vibrant RevBayes group and will contribute to further development of the program. There will be opportunities for the successful applicant to work with and visit the research groups of my collaborators in Europe and the USA. Furthermore, I expect the candidate to become actively involved in our RevBayes workshops and hackathons. I have recently been awarded an Emmy Noether grant from the DFG (German Science Foundation) which will fund at least 3 positions over the next 5 years. This advertisement is for one of these positions and the applicant will start in a young, dynamic and rapidly growing group. My group will be moving to the GeoBio-Center of the LMU Munich, one of Germany’s and Europe’s top Universities (#32 world-wide; #8 in Europe; #1 in Germany; http://bit.ly/2NoZOHs). The GeoBio-Center is located at the Königsplatz which is in walking distance to the historic city center (Marienplatz) and English Garden (city park with 3.75 km² area). The GeoBio-Center is highly interdisciplinary and consists of researchers from different departments including paleontology, molecular and evolutionary biology, zoology and botany. The main research topic for the PhD project is robust estimation of gene trees. Today we have several databases with whole genomes which we would like to use to build phylogenetic trees. However, different genes have different evolutionary histories. To be able to understand why gene trees are discordant, we have to be able to estimate gene trees correctly in the first place. Thus, we need to develop realistic models of the substitution process for each gene. For example, we need to develop and test time-reversible and non-reversible substitution processes, lineage-heterogeneous substitution processes, etc. The foundation of these models is already implemented in RevBayes. The PhD student will apply and explore different substitution models and, depending on the results, develop the next steps for robust gene tree inference. Applicants should have a Master’s degree, completed or completion imminent, in evolutionary biology, computer science, mathematics, statistics, or a related field. Some knowledge and experience in programming (C++, Java, Python or R), phylogenetic inference as well as Bayesian statistics is beneficial. Training in these skills will be provided depending on need. The thesis will be written in English. No knowledge of German is required but some basic knowledge will be helpful outside of work. Enthusiasm, determination and the capacity to work independently are essential. Own ideas complementing the current research direction are highly appreciated. The position will be compensated according to the standard DFG salary scheme (TVL-E13). Note that the position is in Bioinformatics and therefore pays the full 100% salary (compared with the reduced salary in other fields in Germany). The salary is very competitive and includes benefits such as health care, pension, unemployment insurance and child support (if applicable). Further information can be found at ( http://bit.ly/2CsERdu), and questions should be directed to Sebastian Höhna (phylomatics@gmail.com). Applications, including a current CV, letter of motivation (1 page) and names and contact details of two referees should be sent to Sebastian Höhna before the deadline of 31 October 2018. The review process will begin on November 1st and applications will be considered until the position is filled. Sebastian Hoehna via Gmail