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Information on how to enter our PhD programme

A common route for admission into our PhD programme is via the Centre’s MPhil programme in Scientific Computing. The MPhil is offered by the University of Cambridge as a full-time course and introduces students to research skills and specialist knowledge. Covering topics of high-performance scientific computing and advanced numerical methods and techniques, it produces graduates with rigorous research and analytical skills, who are formidably well-equipped to proceed to doctoral research or directly into employment in industry.

List of the groups who offer PhD positions in Scientific Computing and its applications

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Introducing Data Science for Research

PhD students and postdocs across Cambridge University can apply for this training course, which will equip them will skills in data science for science. The next course takes place from 16 October - 17 November. A small number of places are still available, please contact [email protected] if you would like to join.

Aimed at helping participants apply data science tools to their own datasets and research problems, the course runs over five weeks. Previous courses have attracted participants from fields including chemistry, biochemistry, physics, engineering, medicine, veterinary medicine and psychology.

What does the course cover? The course aims to give researchers in fields outside computer science the skills they need to use machine learning (ML) in their research, and help them apply data analysis to their own datasets and problems. On the course, you will: use data science techniques on your own datasets and research questions; collaborate on real-world data science challenges in teams; and contribute to a world-leading community of scientists and researchers. You can read more about how some of our recent participants has used the course to advance their work in the ‘How can I..?’ series on Accelerate’s blog .

Recent participants also share their experiences of the course in a short video available here.

Residents will cover 6 modules over a period of 5 weeks:

• Introduction to Python (preparatory module)
• Data Processing with Pandas
• Data Visualisation
• Web Scraping, JSON and APIs
• Introduction to machine learning
• Project & presentation

What are the entry requirements?

Participants should be current PhD students or researchers at the University of Cambridge and have basic programming skills (e.g. the ability to use Excel, MATLAB, R or Python). Participants should be available from Monday 16 October - Friday 17 November and can expect to spend 30 hours on the programme each week, including timetabled sessions and periods of independent study.

Completion of the Python for Science module is not a pre-requisite for this course, as participants will receive training in the use of Python. However, applicants are required to complete an assessment to demonstrate basic programming skills.

How is the course delivered?

To produce this course, the Accelerate Programme is working with Cambridge Spark, an education technology company that specialises in data science and AI training. For further information about Cambridge Spark, please see: https://cambridgespark.com/about/

The course will be run via Zoom, with sessions delivered by Cambridge Spark and their community of mentors.

How much does the course cost? The course costs £2,100 + VAT per participant. For this cohort, the Accelerate Programme are able to offer 50% funding towards the cost, meaning the course will be available to participants for £1,050 + VAT. Please note that payment arrangements must be confirmed before starting the course.

How can I apply?

A small number of places are still available, please contact [email protected] if you would like to join.

For further questions, please contact [email protected].

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MPhil in Economics and Data Science - Course Modules

Microeconomics Econometric Methods Fundamentals of Data Science Machine Learning in Economics Causal Inference and Machine Learning Research Computing Seminar Series

D001  Economic Analysis of Non-Standard Data D002  Introduction to Algorithmic Trading Robot Design S170  Industrial Organisation S301  Applied Econometrics R301a  Econometrics II: Time Series R301b  Econometrics II: Cross Section and Panel Data S101  Public Economics S130 Economics of Ageing S140  Behavioural Economics S150  Economics of Networks S190 International Trade F300  Corporate Finance

PLEASE NOTE that these are all modules which may be offered, but the Faculty reserves the right to alter, omit or add optional modules within the overall framework described above.

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Cambridge centre for data-driven discovery, c2d3 computational biology.

We are living in a very exciting time for biology: whole-genome sequencing has opened up the field of genome-scale biology and with this a trend to larger-scale experiments, whether based on DNA sequencing or other technologies such as microscopy.  However it is also a time of great opportunity for small-scale biology as there is a new wealth of data to build from: one can turn to a computer to ask questions that previously might have taken months to answer in the laboratory. One of the great challenges for the field is analysing the large amounts of complex data generated, and synthesising them into useful systems-wide models of biological processes. Whether operating on a large or small scale the use of mathematical and computational methods is becoming an integral part of biological research.

There remains a world-wide shortage of skilled computational biologists. An important part of C2D3 Computational Biology is an MPhil course based at the Centre for Mathematical Sciences. The 11-month course introduces students to bioinformatics and other quantitative aspects of modern biology and medicine. It is intended especially for those whose first degree is in mathematics and computer science and others wishing to learn about the subject in preparation for a PhD course or a career in industry. Complementing the MPhil course is the Wellcome Trust PhD programme in Mathematical Genomics and Medicine.  Run jointly with the Wellcome Trust Sanger Institute this programme provides opportunities for collaborative research across the Cambridge region at the exciting interfaces between mathematics, genomics and medicine.

History and financial support 

C2D3 Computational Biology came about by the merger of the Cambridge Computational Biology Institute (CCBI) into C2D3 in 2021. The CCBI was established in 2003 to promote computational biology, interpreted broadly, within the University and in the region. It established (2004) the MPhil in Computational Biology programme, founded (2011) the Wellcome Trust Mathematical Genomics and Medicine 4-year PhD programme, and, among other activities, started a popular computational biology annual symposium. The CCBI was involved in setting up and helping to run the Cambridge Big Data (CBD) Strategic Research Initiative out of which the C2D3 Interdisciplinary Research Centre was formed. Similarly the CCBI was part of the group that helped set up the Alan Turing Institute.  

The CCBI received financial support equally from the four science schools of the University: 

• The School of the Biological Sciences      
• The School of Clinical Medicine      
• The School of the Physical Sciences (via DAMTP, Physics, Chemistry)      
• The School of Technology (via Engineering, Computer Science) 

Space was kindly provided by the Department of Applied Mathematics and Theoretical Physics, within the Centre for Mathematical Sciences. 

MPhil in Computational Biology  

The Cambridge-MIT Institute provided funds to establish the MPhil in Computational Biology and subsequently studentships have been provided by: 

• Biotechnology and Biological Sciences Research Council      
• Cancer Research UK      
• Engineering and Physical Sciences Research Council      
• Medical Research Council      
• Microsoft Research 

MGM PhD Programme 

The PhD programme in Mathematical Genomics and Medicine is funded by the Wellcome Trust.

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Quantitative Biology Seminar

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The evolution of gene regulation, compensation and expression noise

Intrinsic disorder promotes protein refoldability and enables retrieval from biomolecular condensates.

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The Cambridge Centre for Data-Driven Discovery (C2D3) brings together researchers and expertise from across the academic departments and industry to drive research into the analysis, understanding and use of data science and AI. C2D3 is an Interdisciplinary Research Centre at the University of Cambridge.

• Supports and connects the growing data science and AI research community 
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• Drives new research challenges through collaborative research projects 
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Applications open for new 'Data Science For Science' course

Submitted by Rachel Gardner on Tue, 06/12/2022 - 12:46

PhD students and postdocs across Cambridge University can apply for a new training course that will equip them with skills in using data science to advance their own scientific research.

The course is being run by our colleages at the accelerate programme for scientific discovery , which is based here in the department of computer science and technology..

My research focuses on harnessing the power of AI to help clinicians make data-driven decisions about postoperative bleeding. The Data Science for Science Residency helped me with... practical data science processes essential for getting data into a usable format in order to visualise, analyse and start to build AI models. PhD candidate Diana Robinson

Starting on 27 February 2023, the new five-week course is aimed at helping participants apply data science tools to their own datasets and research problems.  Applications are now open and will close at 5:00 pm on Friday 13 January 2023.

Previous courses have attracted participants from many fields. They include archaeology, chemistry (like Ryan Geiser, pictured above, who wanted to use AI in his PhD research into Alzheimer's Disease), physics, engineering, medicine, veterinary medicine and psychology. Participants have been looking for answers to a range of questions, including:

• How can we... use AI to enable doctors to build their own models with clinical data?     
• How can we…create next-generation solar technologies using machine learning?    
• How can we…understand the crafts of ancient communities using machine learning?

Practical data science skills The course aims to give researchers in fields outside computer science the skills they need to use machine learning (ML) in their research, and help them apply data analysis to their own datasets and problems. Many of them have found this extremely helpful in advancing their work:

During the course, participants will use data science techniques on their own datasets and research questions and collaborate on real-world data science challenges in teams. They will also contribute to a world-leading community of scientists and researchers. You can see and hear from some of them in the video below.

See full details, including how to apply, on the Accelerate Programme website .

Data Science for Science course

How long does it run? 5 weeks.

When does it start? Monday 27 February 2023

Who can apply? Cambridge University PhD students or researchers with basic programming skills.

What's the time commitment? 30+ hours per week for five weeks. 

How much does it cost? £1,050 (plus VAT). 

How do I apply? Online via the Accelerate Science website .

What's the deadline to apply?  5:00 pm, Friday 13 January 2023.

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The PhD degree is the Department of Earth Sciences' principal research degree for postgraduate students. As a large and integrated department, the expertise and current research of our staff spans the breadth of Earth Sciences. We have 37 academic staff who are available to supervise PhD students. 

We welcome research enquiries from students who are currently working toward, or have acquired, first degrees in: Earth Science subjects, physics, chemistry, mathematics, material science, biology, or other related subjects.

The Department of Earth Sciences is a partner in two Doctoral Training Programmes (outlined below), who award research-council-funded studentships. Other studentships are available through different funding sources. 

If you wish to find out more about a project or the Department, or want to discuss devising your own project with us, then please contact a relevant member of academic staff —you can discover their interests on our Research pages .

Cambridge CREATES DTP

Environmental science is at the heart of our most pressing societal challenges: climate change and the need for secure energy; biodiversity loss in the context of food production and land use pressures; natural and climate-induced hazards in the face of growing vulnerability.

The Cambridge Research Experience and Advanced Training for Environmental Scientists ( CREATES ) Doctoral Landscape Award (DLA) unites the University of Cambridge and the British Antarctic Survey as hosts for PhD training and projects, working with a wide range of collaborative partners. Its aim is to nurture and train environmental scientists from a variety of backgrounds, creating diverse cohorts of interdisciplinary, problem-solving environmental science postgraduates qualified to take up a broad spectrum of careers. DLA is the new terminology for Doctoral Training Partnerships (DTPs), and CREATES is our new programme, which we hope will take in 5 cohorts of students, with the first cohort starting in October 2026. CREATES follows on from C-CLEAR which recruited 6 cohorts of students. Various departments of the University of Cambridge, as well as the British Antarctic Survey, are members of CREATES and eligible to host PhD students. Students will apply to work in broad areas with a named lead supervisor and will co-develop detailed PhD projects with full supervisory teams (and potential CASE partners) on arrival.

Cambridge AI4ER CDT

The Cambridge UKRI Centre for Doctoral Training in the Application of Artificial Intelligence to the study of Environmental Risks ( AI4ER )  offers around ten 4-year UKRI-funded PhD studentships each year to start in October. The programme comprises a one-year MRes (two terms taught, one term research), and a three-year PhD to apply AI methodologies.

A wide range of projects will be available under the broad themes of:

• Weather, climate, and air quality
• Natural hazards
• Natural resources (food, water and resource security, and biodiversity)

For more information on this Centre for Doctoral Training, including training structure and applying to the course, please visit the  AI4ER CDT pages .

Fully-funded studentships are also available at the  BPI Institute , and through the EPSRC Centre for Doctoral Training in Nuclear Energy Futures .

We are also happy to devise projects with you, particularly if the projects outlined above are not of interest and you have interests that we share. Explore our  Research pages to see which members of academic staff you would like to work with, and then contact them directly.

DTP studentships will be funded by UK research councils. Other studentships available in department will be funded by industry and several Cambridge Colleges. 

We also have a number of CASE awards, which involve direct links with industry partners.

Applications

Before applying, applicants are advised to contact the relevant member of academic staff for their chosen project to discuss your research interests.

To make a formal application for a PhD studentship, please go to the  University's Applicant Portal . When you complete the on-line application, you will have to indicate a college choice—it may help to discuss this choice with your prospective supervisor before submitting your application.

If you are applying from outside the UK, then please read our  PhD (Overseas Students)  page.

For questions related specifically to a project, please contact the relevant supervisor directly.

For more general information, please contact our  Postgraduate Admissions .

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Cambridge C-CLEAR DTP—Projects

EPSRC CDT in Nuclear Energy Futures

Postgraduate Admissions

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Postgraduate Admissions—Funding

Postgraduate Admissions—International

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Limited-time offer: Coaching until you reach your goals

Register and pay for the upcoming intake of the Career Accelerator before 10 June and get free, extended 1:1 coaching until you achieve your career goal.

Intake starts 7 October: Limited places available

Data science with machine learning & ai career accelerator, gain advanced data science skills, hands-on project experience, and industry insights to excel in your career., programme duration: 7 months, 100% online start date: 7 october 2024 apply by: 30 september 2024.

Limited places available - past intake fully subscribed

Machine Learning and AI Focus

Gain advanced skills in Machine Learning and AI | Covering topics like NLP, time series analysis, and generative AI

Time commitment

20 hours per week | 7 months online learning (plus break weeks)

One-to-one career coaching | plus bi-weekly mentoring with industry experts

Portfolio development

Work on 20+ industry-relevant projects | including a 6-Week project with the Bank of England

Payment plans

Flexible payment options available | 6-9 months + StepEx (Interest-free payment plan)

Discover the future of data science

Watch the programme trailer to see how the Data Science With Machine Learning & AI Career Accelerator can transform your career. Learn about the advanced skills, industry projects, and personalised support that sets our programme apart.

Why choose this Career Accelerator

Our programme ensures that your data science skills are not only advanced but also aligned with real-world business needs, providing you with a competitive edge. Throughout this Career Accelerator, you will:

Develop a portfolio of real-world projects based on challenges set by leading employers. Work on 20+ industry-relevant projects, including a 6-week project with the Bank of England, to showcase your practical skills and expertise.

Learn the advanced tools, techniques, and skills from the foremost academic and industry practitioners, currently being promoted by data professionals. Gain hands-on experience with cutting-edge technologies and methodologies used in today's data science and AI landscape.

Master the essential statistical concepts and principles to future-proof your data science career in the era of AI and Machine Learning. Understand and apply advanced statistical techniques and machine learning algorithms to solve complex business problems.

Cultivate your ability to think commercially by tackling business challenges presented throughout the programme. Learn to align data science solutions with business objectives to drive impactful results.

Become a more holistic practitioner by understanding how to make data science models implementable within business. Learn the practical aspects of deploying machine learning models and ensuring their scalability and efficiency in real-world scenarios.

Set yourself apart by demonstrating legal, moral, and ethical responsibility and awareness of cutting-edge technologies. Gain insights into the ethical considerations and regulatory requirements associated with data science and AI applications.

Deep dive into Generative AI and Large Language Models in Course 4. Explore the latest advancements in Generative AI, including instruction tuning, reinforcement learning from human/AI feedback, and parameter-efficient fine-tuning, preparing you for the future of data science.

What our learners say

The Career Accelerator has opened up a whole world that I never knew was out there before. I love what I do… but sometimes I felt like, “what’s next?“ But now there’s so much opportunity – so many doors have opened up for me…

Haroon Miah - Career Accelerator Graduate

Programme Details

Learn the advanced tools, techniques, and skills that are currently getting data professionals promoted.

Applying Statistics and Core Data Science Techniques in Business

Develop critical statistical thinking and problem-solving skills.

Learn how to apply unsupervised learning to solve business problems.

Gain proficiency in feature engineering and statistical analysis to drive business insights.

Solving Business Problems with Supervised Learning

Master supervised learning techniques including regression, classification, and ensemble methods.

Build and optimise machine learning models to generate actionable business insights.

Explore deep learning methods to uncover hidden patterns in complex datasets.

Applying Advanced Data Science Techniques

Dive deeper into advanced machine learning models, NLP and Time Series Analysis.

Understand the intricacies of neural networks and deep learning.

Leverage frameworks like TensorFlow for efficient model development and tuning.

Exploring the Future of Data Science + Capstone Employer Project

Engage in advanced topics in Generative AI and other large language models (LLMs).

Learn cutting-edge techniques like instruction tuning, reinforcement learning from human/AI feedback, and parameter-efficient fine-tuning.

Work on a culminating project to showcase your skills and competencies, collaborating with leading employer partners on real-world data science problems.

Tools & Languages:

Google Colab, GitHub, Python, TensorFlow, LangChain, Hugging Face and more

Develop a portfolio of real-world projects based on challenges set by leading employers

We are thrilled to be collaborating on the Employer Project to not only provide the learners with the unique opportunities to practise data science on offer at the Bank of England, but to also give us the opportunity to reach a broader pool of potential talent from diverse backgrounds and experience who we know have the skills we need to help us achieve our mission.

James Benford Chief Data Officer at Bank of England

Capstone Project with the Bank of England

Apply your knowledge to real-world problems using Generative AI and LLMs. Present your findings to industry professionals and receive invaluable feedback.

Generative AI and LLMs:

Explore the latest advancements in Generative AI, including instruction tuning datasets, reinforcement learning, and low-rank adaptation for fine-tuning models.

Frameworks and Methodologies:

Learn about frameworks like Langchain, retrieval-augmented generation (RAG), and techniques to enhance and assess the performance of language models.

Real-World Applications:

Apply your knowledge to a significant business problem, presenting your findings to industry professionals and gaining invaluable feedback..

This Data Science Career Accelerator from the University of Cambridge Institute of Continuing Education is focused on shaping the future of business through data. It's not just about algorithms, statistics, or utilising AI and Machine Learning techniques; it’s also about generating meaningful, actionable insights that can challenge conventional wisdom and enable commercial success

Dr. Ali Al-Sherbaz Assistant Professor and Academic Director for Digital Skills courses at University of Cambridge Institute of Continuing Education

Learn from Leading Experts

This programme brings together academic and industry perspectives to design, build and deliver a curriculum that represents the best of both worlds.

Dr. Ali Al-Sherbaz PhD, MSc, BSc, Electronic and Communications Engineering

Assistant Professor in Digital Skills at University of Cambridge Institute of Continuing Education

Author of more than 80 peer-reviewed papers with expertise in Cybersecurity, IoT, Data Science, AI, Blockchain and 5G. Passionate about guiding research and innovation strategies.

Shanup Peer MBA, Operations/Marketing, MS, Electrical Engineering, B.Tech, Electrical and Electronics Engineering

Data Scientist and Programme Industry Expert

Principal Data Scientist, AI Curriculum Architect and Data Science Mentor, having fulfilled engagements with Government entities and corporate clients, developing technology solutions that have been deployed on a country-wide scale.

Robert Hardman Chief AI & Innovation Transformation Officer, Inchcape Digital

Robert is an industry trailblazer with a career spanning over 25 years working with Fortune 100 companies, such as Facebook and Uber, guiding them through digital business transformations. His command over advanced mathematical techniques and knowledge of global technological ecosystems has made him a specialist in employing state-of-the-art technologies such as Generative AI, LLM’s, ML to transform & reimagine businesses.

Dr. Alexia Cardona BSc, MSc, PhD

Training Programme Lead in Data Science at Newnham College, University of Cambridge.

Alexia is also a Tutor and Postgraduate Mentor at Newnham College, and a Senior Teaching Associate in the Department of Genetics. Her research interests focus on teaching and learning in the areas of data science, reproducibility, and Bioinformatics.

Dr Giovanna Maria Dimitri

Assistant Professor in Deep Learning and Artificial Intelligence, University of Cambridge

Giovanna is a researcher at the University of Siena. She completed her Master's and PhD in Computer Science at the University of Cambridge under the supervision of Prof. Pietro Liò, focusing on Artificial Intelligence and Machine Learning for biomedical data processing at the Department of Computer Science. She has a research publication record of over 45 papers in peer-reviewed and international journals, as well as broad experience in teaching and supervising. She has been interviewed by several journals and TV shows in Italy for her expertise in Artificial Intelligence and Computer Science and has considerable experience in science communication events. Her research interests focus on artificial intelligence, in a wide spectrum of applications, as well as in the development of foundational models.

Dr Russell Hunter

PhD Computational Neuroscience, Senior Teaching Associate (Online Education and Web Technology), Department of Engineering, University of Cambridge

Dr Hunter's varied career has spanned industry, research and teaching. His PhD was in the field of Computational Neuroscience, and his research has focused on image processing and computer vision in Formula One motor racing. He continues his research as a Post Doctoral Candidate in the Department of Engineering, developing novel educational tools. Dr Hunter is also R&D in a personalisation team, working with big data, data science, and machine learning to develop industry-first products from end-to-end. In this role, he leads on innovation strategy.

Jon Howells

AI and Data Science professional and founder of AI consultancy, Qualifai

Jon Howells is a seasoned AI and Data Science professional with a decade of experience in the field. He runs an AI consultancy called Qualifai and is currently working on a book titled "Data Science for Decision Makers" . Jon has worked with various companies, including Nestlé, Unilever, and Capgemini, developing and deploying data science services and solutions. He holds a Master's degree in Computational Statistics & Machine Learning from UCL. Jon is particularly interested in the application of Large Language Models (LLMs) in consumer-focused businesses, such as leveraging LLMs for consumer research and feedback analysis, personalised content generation, and enhanced customer support, ultimately helping businesses better understand and engage with their customers.

Jack Gannaway

Data Analytics Leader and founder, Future Consulting

Jack Gannaway has been working in data analytics and data science across the UK and The Netherlands for the past 16 years. Spanning roles in both the public and private sector, his work has focussed on how to support decision-making with data. Throughout his career Jack has worked with a huge variety of data, models and methodologies including demand modelling, b2b cross-sell prediction, predicting bankruptcy using accounting data, discrete event simulation and his favourite, dynamic stochastic microsimulation.

Diwakar Patwal

Head of Data Science and Chief Data Officer, Doji

Diwakar is leading the Data and AI practice at Doji, a UK based tech start-up which is reinventing the way consumers buy and sell refurbished electronics. He has had a long global career in building Data Science and AI solutions across the financial services, e-commerce and logistics industry.Developing complex ML and AI solutions in marketing optimisation, operational automation, customer engagement and logistics optimisation have been some of his career highlights. He has leveraged advanced machine learning techniques such as Computer Vision, Natural Language Processing, LLMs and Supervised ML models to deliver effective and impactful solutions.

Alexander Smirnov

Data Science Consultant

Alex advises merchants and banks on applying data science to improve their sales performance. In his previous roles Alex consulted clients on the issues of analytics and finance across multiple sectors, including energy, transport, and fixed income markets. Alex holds an MSc Financial Economics from Oxford and MSc Machine Learning from UCL. He has also successfully completed all CFA and CAIA examinations.

What is the cost of this programme?

The tuition fee for this programme is £8,395 £7,895* (upfront). Benefit from a reduced rate by making payment upfront, prior to the start of the programme, or ask an Enrolment Advisor about our flexible payment plans.

Do you have flexible payment options available?

We have a variety of flexible payment options available for this programme. We also offer StepEx , a 24-month, interest-free payment plan.

Do I need to attend classes at certain times?

The majority of learning requirements can be completed asynchronously, in your own time. While attendance is strongly recommended for live webinar sessions and masterclasses, recordings will be made available at the conclusion of each session. Live classes run mid-week and outside of standard working hours.

Why should I choose a Career Accelerator?

Career Accelerators deliver the technical skills needed to perform in real-world roles, the understanding necessary to translate those abilities into meaningful value, the human skills crucial for gaining and retaining jobs, and the reflective capabilities essential for ongoing growth.

How is the programme delivered?

The programme is delivered entirely online through a virtual learning environment which is accessible from your personal computer or smartphone. Your personal success manager will be available to offer support and guide you through your learning journey.

What are the entry requirements?

This is an advanced data science programme, therefore there are a few prerequisites before the learner can enrol, these include:

• an undergraduate degree that features at least one unit of quantitative studies (such as mathematics, statistics, accounting or finance)
• at least 1 year of work experience in a field broadly related to data or business analytics
• an IELTS certificate with an overall band score of 7.0 with no less than 7.0 in speaking, listening, and writing, and 6.5 in reading – if English is not your first language
• the ability to interpret data and analytic output and draw conclusions that help to inform data-driven decision-making
• basic Python programming skills (if you don't have these skills yet, don't worry, we have a few free courses to get you prepared)
• practical experience in accessing, manipulating, querying, and analysing data using a range of software and tools, including Python for data analysis (e.g. NumPy, Pandas, scikit-learn), Python for data visualisation (e.g. matplotlib, seaborn) and SQL
• knowledge and application of statistical principles for descriptive analysis.

In some cases, we can be flexible with certain requirements. For example, if you lack an undergraduate degree we may still be able to accept your application if you are able to demonstrate equivalent professional experience.

If you are unsure whether you meet the requirements, please speak to one of our enrolment advisors.

Do I need to provide any supporting documents?

As part of the application process you will be required to provide a copy of an academic transcript, a one-page CV or LinkedIn profile, an IELTS certificate (if relevant) and a personal statement (up to 500 words). Depending on your education history and work experience you may be required to pass a short technical test.

What is the role of industry and employer partners in the Career Accelerator?

The employer partners add industry experience to coursework development, share tech expertise, and play a direct role in the portfolio project design. Through their insight into the ever-changing landscape in the digital economy, they ensure students develop skills aligned to workplace demand, equipping them for job opportunities.

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PhD Conference 2024: Social Science Approaches to Crime, Harm, and (In)justice

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We are pleased to announce our second PhD conference at the Institute of Criminology, University of Cambridge.  This is an excellent opportunity for PhD researchers across the UK to present their work and engage with others working in criminology and related disciplines.

We invite abstract submissions that critically and creatively engage with the themes of crime, harm, and (in)justice. This includes empirical and theoretical contributions, methodological and ethical considerations, and implications of your research for policy and practice.  

This event has now passed.  Read the summary here .  

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Engineering

Data science, master of science in data science.

Accelerate your upward career mobility with a master’s degree in data science from the Virginia Commonwealth University (VCU). Jointly operated by the VCU College of Engineering and the Department of Statistical Science and Operations Research in the College of Humanities and Sciences, coursework will encompass theory and application of data science techniques.

Technological advancements and the proliferation of collected data have enabled businesses to use information for more precise strategic decisions. Because of these trends, data science continues to evolve as a distinct discipline.

Our program teaches the advanced knowledge necessary to employ tools and strategies for the analysis and interpretation of complex data to solve real-world problems. Become part of this rapidly growing field with graduate-level education focused on interdisciplinary coursework in statistics, computer science and industry-specific knowledge tailored to your interests. Students seeking to work in a healthcare-related field, for example, would develop industry-specific medical knowledge in order to interpret data effectively.

We are among the best graduate programs in the nation as ranked by U.S. News and World Report . Combined with our industry connections and access to Richmond-area businesses, VCU Engineering is a solid choice for your continuing education.

What you’ll learn

Our 30-credit program can be completed in about two years by full time students. It emphasizes ethical considerations, real-world projects and integration of industry-specific information, focusing on effective data communication and culminating in a two-semester practicum experience serving a client. Your knowledge of business, manufacturing and research will also grow to help advance your career and provide a mechanism for lifelong learning and professional development. Graduates will be prepared to process data with computational methods and advanced algorithms for any business that generates data for decision-making.

Through hands-on opportunities, the advanced computing skills you master will be complemented by an ability to communicate effectively with stakeholders and apply advanced problem solving to computing challenges in cross-disciplinary teams.

The master’s in data science at the VCU College of Engineering will teach you about:

• Data manipulation
• Machine learning algorithms
• Data visualization
• Big data technologies
• Database management 

You will also learn programming languages like: 

Virginia has a significant need for qualified data scientists. According to The Virginia Economic Development Partnership, the state has a high concentration of employers seeking qualified data scientists. In 2020, the recruitment platform Zippia ranked Virginia as a top location for companies actively looking for data scientists.

Etched into the landscape of Richmond, the commonwealth's capitol, The VCU College of Engineering gives students access to a culturally vibrant and diverse city full of potential. We focus on developing close partnerships with public institutions and private businesses in order to give you unique learning and job opportunities.

Our proximity to Virginia policymakers and the importance of supplying industry with capable data scientists will position you to make the most of VCU’s partnerships.

Master’s program students also have access to benefits like:

• Applying classroom knowledge to real-world problems through a two-semester practicum experience serving a client. Working professionals can choose to design their project around existing duties with their employer.
• Cooperation on A.I. data science projects through access to VCU programs employing the technology.
• In-depth learning of statistical analysis theory and application through joint operation with the Department of Statistical Science and Operations Research in the College of Humanities and Sciences.
• Dedicated Career Services department that provides internship and employment opportunities.
• Industry connections through college partnerships with public and private industry.
• Interdisciplinary education to teach collaboration with engineering practitioners outside your field of study.

Study of foundational data science topics and their application is the focus of the master’s program curriculum. Reference the VCU Bulletin for a full list of data science classes. Master’s program courses are 500 level and above (for example, CMSC 502). Below are a few signature courses from the program:

• Advanced Natural Language Processing (CMSC 516) : Learn about recent advances in natural language processing and apply this knowledge to the processing of unstructured text using natural language processing algorithms. You will study word-level, syntactic and semantic processing in addition to topicslike rule-based and statistical methods for creating computer programs that analyze, generate and understand human language; regular expressions and automata; context-free grammars; probabilistic classifiers; and machine learning. Apply your knowledge to real-world problems like spell-checking, Web search, automatic question answering, authorship identification and developing conversational interfaces.
• Introduction to Machine Learning (CMSC 606) : You will gain a foundational understanding of machine learning and recent advances in modern machine learning approaches, like deep learning. Topics covered include: automated differentiation for machine learning, linear models based on maximum likelihood, feedforward deep models and techniques for improving effectiveness and efficiency of training models. The course also covers specialized deep architectures like convolutional networks, generative models and large language models.
• Statistical Data Analysis (STAT 534) : You will become familiar with processing different data types from multiple sources; presentation of complex data; programming, statistical and machine learning algorithms (like maximum likelihood and least squares); design, implementation and analysis of simulation studies; and other topics that reflect the current needs of data scientists.

Data science can be applied to many fields, infusing your career with near limitless opportunity. The VCU College of Engineering master’s in  data science can facilitate career advancement in fields like:

• Computer science
• Manufacturing
• Mathematics
• Pharmaceuticals

With a master’s in data science from the VCU College of Engineering, you will be ready for roles like:

• Business Intelligence Analyst: Help stakeholders create actionable strategies from collected data to increase a company’s efficiency and maximize profits. Parse large amounts of data using effective database querying to produce reports and identify trends that generate actionable business insights.
• Data Scientist: Gather and interpret data to solve a specific problem or model and present data to convey key information to stakeholders.
• Machine Learning Engineer: Build large-scale software systems for processing massive data sets, using these systems to train algorithms capable of learning cognitive tasks and generating useful insights and predictions. Machine learning engineers manage the entire data science pipeline, including sourcing and preparing data, building and training models, and deploying models to production.

With the help of our Career Services team, VCU College of Engineering graduates have many opportunities to network with alumni and industry professionals. Our students work at companies like:

• Black Knight Technology Inc.
• Blue River Technology
• Capital One
• CoStar Group
• Federal Reserve Bank of Richmond
• Micron Technology Inc.
• MITRE Corporation
• NT Concepts

How to apply

VCU offers an online, self-managed application process. See what’s needed to apply for an engineering graduate program and reference our list of Frequently Asked Questions (FAQ) .

Start your application

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PhD in Biological Science (EBI)

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Established in 1983, the European Molecular Biology Laboratory (EMBL) International PhD Programme provides students with the best starting platform for a successful career in science. Characterised by first-class training, internationality, dedicated mentoring and early independence in research, it is among the world's most competitive PhD training schemes in molecular biology. All of EMBL's six outstations participate in the programme.

EMBL's European Bioinformatics Institute (EMBL-EBI) provides a highly collaborative, interdisciplinary environment in which research and service provision are closely allied. We are a world leader in bioinformatics research and service provision, as we are at the centre of global efforts to collect and disseminate biological data. We share a campus with the Wellcome Sanger Institute, 12 miles south of Cambridge in the United Kingdom. PhD students at EMBL-EBI are members of the University of Cambridge and one of its Colleges. They receive their degree from Cambridge University; the programme is coordinated in Heidelberg with local support at EBI. Please visit the EMBL International PhD Programme pages to learn about how to apply. Please note all applicants must secure a place on the EMBL programme before submitting an application to the University of Cambridge.

EMBL PhD students receive theoretical and practical training and conduct a research project under the supervision of an EMBL faculty member, monitored by a thesis advisory committee. The duration of PhD studies is normally three-and-a-half to four years.

In Year 1 all new PhD students will attend the EMBL Predoctoral Core Course in Molecular Biology in Heidelberg; attend Primers for Predocs; undergo nomination of a thesis advisory committee to monitor student progress, and submit and defend a project proposal.

In Year 2 students will need to submit a second annual report to the thesis advisory committee, participate in the Bioinformatics course and predoc seminar day.

In Year 3 students will need to submit a third annual report to the thesis advisory committee.

In Years 3/4 students will need to write and submit their thesis:  PhD awarded following Degree Committee approval and successful completion of an oral examination.

The Postgraduate Virtual Open Day usually takes place at the beginning of November. It’s a great opportunity to ask questions to admissions staff and academics, explore the Colleges virtually, and to find out more about courses, the application process and funding opportunities. Visit the  Postgraduate Open Day  page for more details.

See further the  Postgraduate Admissions Events  pages for other events relating to Postgraduate study, including study fairs, visits and international events.

Key Information

3-4 years full-time, study mode : research, doctor of philosophy, european bioinformatics institute, course - related enquiries, application - related enquiries, course on department website, dates and deadlines:.

Some courses can close early. See the Deadlines page for guidance on when to apply.

Easter 2025

Michaelmas 2025, easter 2026, similar courses.

• Biotechnology MPhil
• Biological Sciences (Developmental Biology) by advanced study MPhil
• Medical Science (CRUK CI) MPhil
• Biological Science (MRC Laboratory of Molecular Biology) PhD
• Medical Science (CRUK CI) PhD

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C2D3 ECR and student conference 2024

C2D3 seeks to create an interdisciplinary data science and AI community for Early Career Researchers (ECRs) and students, as a place for supporting researchers and their ideas, sharing solutions and networking. 

This half-day conference will provide a forum to exchange ideas, discuss research problems and solutions, and make new connections. During the conference we will hear presentations from the C2D3 ECR Seed Fund Awardees (2023 and 2024) and lightning talks from the ECR and student community. 

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Artificial intelligence outperforms clinical tests at predicting progress of alzheimer’s disease, cambridge scientists have developed an artificially-intelligent tool capable of predicting in four cases out of five whether people with early signs of dementia will remain stable or develop alzheimer’s disease..

We’ve created a tool which is much more sensitive than current approaches at predicting whether someone will progress from mild symptoms to Alzheimer’s Zoe Kourtzi

The Cambridge Centre for Data-Driven Discovery (C2D3) brings together researchers and expertise from across the academic departments and industry to drive research into the analysis, understanding and use of data science and AI. C2D3 is an Interdisciplinary Research Centre at the University of Cambridge.

• Supports and connects the growing data science and AI research community 
• Builds research capacity in data science and AI to tackle complex issues 
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Working together, we can reimagine medicine to improve and extend people’s lives.

Principal Scientist with PhenoCycler Fusion experience (PhD)

About the role.

Internal Job Title: Principal Scientist I/II

Position Location: Cambridge, MA, onsite

About the Role:

We are seeking a highly motivated individual passionate about cutting-edge technology to explore single cell multiplex spatial proteomics. This role involves working with the latest generation PhenoCycler Fusion instrument and collaborating with translational immunologists, cancer biologists, and other researchers to advance our understanding of cellular processes in complex tissues and their application to drug development. This role offers exciting opportunities for career development, enhancing leadership skills and influencing collaborative efforts within various disease areas.

Key Responsibilities:

• Operate, maintain, and utilize the PhenoCycler Fusion (formerly CODEX).
• Build and optimize antibody panels.
• Conjugate and perform quality control of reagents.
• Consult with users on potential projects, including sample accessibility and experimental design.
• Optimize procedures, design panels, and provide data analysis consultation.
• Conduct multiplex imaging experiments.
• Perform basic data quality evaluation.
• Analyze data using licensed software.
• Maintain records of procedures and resultant data, both manually and on the computer.

Knowledge, Skills, and Abilities:

• Serve as a leader in spatial proteomic single cell biology and translational research applications, focusing on new targets, biomarkers/patient population selection, and treatment strategies.
• Focus efforts in priority application areas in Biomedical Research (BR) at Novartis to deliver impactful results through matrix collaboration with DA teams.
• Building on success from initial focused efforts, develop broader application strategies at BR in translational and reverse translation research, with support from leaders in Discovery Science, Disease Areas and Biomedical Research.
• Strong interpersonal and communication skills for close collaboration with team members.
• Ability to work effectively in a fast-paced, diverse environment.
• Good judgment, technical problem-solving, and analytical skills.
• Flexibility and adaptability as technology evolves.
• Prior experience in imaging techniques and applications in biological research.
• General lab skills and knowledge of lab safety and infection control.

Qualifications:

• Ph.D. in immunology, biological sciences, biochemistry, or a related field, and 2+ years of related postgraduate work experience
• Other technical and academic degrees will be considered with relevant research experience.
• 3+ years of demonstrated skill and experience using CODEX/PhenoCycler.
• Possess deep knowledge and expertise in immunology, biology, and multi-omics applications in translational research across various disease areas such as oncology (ONC), immuno-oncology (IO), immunity-driven diseases, and related treatment strategies.
• Understanding sample preparation, instrument optimization, and data analysis.
• Interest in bioinformatics and experience with software.
• Ability to identify and troubleshoot critical issues.
• Detail-orientated

Why Novartis: Our purpose is to reimagine medicine to improve and extend people’s lives and our vision is to become the most valued and trusted medicines company in the world. How can we achieve this? With our people. It is our associates that drive us each day to reach our ambitions. Be a part of this mission and join us! Learn more here: https://www.novartis.com/about/strategy/people-and-culture

You’ll receive: You can find everything you need to know about our benefits and rewards in the Novartis Life Handbook: https://www.novartis.com/careers/benefits-rewards

Commitment to Diversity and Inclusion / EEO: The Novartis Group of Companies are Equal Opportunity Employers and take pride in maintaining a diverse environment. We do not discriminate in recruitment, hiring, training, promotion or other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, gender identity or expression, marital or veteran status, disability, or any other legally protected status. We are committed to building diverse teams, representative of the patients and communities we serve, and we strive to create an inclusive workplace that cultivates bold innovation through collaboration and empowers our people to unleash their full potential.

Novartis Compensation and Benefit Summary: The pay range for this position at commencement of employment is expected to be between $112,800 to $186,000/year; however, while salary ranges are effective from 1/1/24 through 12/31/24, fluctuations in the job market may necessitate adjustments to pay ranges during this period. Further, final pay determinations will depend on various factors, including, but not limited to geographical location, experience level, knowledge, skills, and abilities. The total compensation package for this position may also include other elements, including a sign-on bonus, restricted stock units, and discretionary awards in addition to a full range of medical, financial, and/or other benefits (including 401(k) eligibility and various paid time off benefits, such as vacation, sick time, and parental leave), dependent on the position offered. Details of participation in these benefit plans will be provided if an employee receives an offer of employment. If hired, employee will be in an “at-will position” and the Company reserves the right to modify base salary (as well as any other discretionary payment or compensation program) at any time, including for reasons related to individual performance, Company or individual department/team performance, and market factors.

Join our Novartis Network: If this role is not suitable to your experience or career goals but you wish to stay connected to hear more about Novartis and our career opportunities, join the Novartis Network here: https://talentnetwork.novartis.com/network

Commitment to Diversity and Inclusion: Novartis is committed to building an outstanding, inclusive work environment and diverse teams' representative of the patients and communities we serve.

Why Novartis: Helping people with disease and their families takes more than innovative science. It takes a community of smart, passionate people like you. Collaborating, supporting and inspiring each other. Combining to achieve breakthroughs that change patients’ lives. Ready to create a brighter future together? https://www.novartis.com/about/strategy/people-and-culture

Join our Novartis Network: Not the right Novartis role for you? Sign up to our talent community to stay connected and learn about suitable career opportunities as soon as they come up: https://talentnetwork.novartis.com/network

Benefits and Rewards: Read our handbook to learn about all the ways we’ll help you thrive personally and professionally: https://www.novartis.com/careers/benefits-rewards

EEO Statement:

The Novartis Group of Companies are Equal Opportunity Employers who are focused on building and advancing a culture of inclusion that values and celebrates individual differences, uniqueness, backgrounds and perspectives. We do not discriminate in recruitment, hiring, training, promotion or other employment practices for reasons of race, color, religion, sex, national origin, age, sexual orientation, gender identity or expression, marital or veteran status, disability, or any other legally protected status. We are committed to fostering a diverse and inclusive workplace that reflects the world around us and connects us to the patients, customers and communities we serve.

Accessibility & Reasonable Accommodations

The Novartis Group of Companies are committed to working with and providing reasonable accommodation to individuals with disabilities. If, because of a medical condition or disability, you need a reasonable accommodation for any part of the application process, or to perform the essential functions of a position, please send an e-mail to [email protected] or call +1(877)395-2339 and let us know the nature of your request and your contact information. Please include the job requisition number in your message.