PhD Studentship in Biomedical Data Science

A 3-year studentship in the application of biomedical data science to understand the aetiologies of common diseases, create risk prediction models, and develop open computational tools and resources.

The studentship is funded by the Health Data Research (HDR) UK Molecules to Health Records (MTHR) grant at the University of Cambridge.

Background: The expanding wealth of biomolecular and health data in human cohorts worldwide is transforming how we do research and the impact that it has. We are discovering new drug targets, identifying new drugs, repurposing existing drugs, creating new risk prediction and prevention strategies and guiding the design of clinical trials. Our research groups work at the cutting edge of genomics, multi-omics, machine learning and epidemiology to develop and apply multi-purpose molecular tools, notably polygenic scores, to gain insights into the molecular underpinnings of disease and improve disease risk prediction.

We practice Open Science (e.g. FAIR Data Principles and preprint/rapid publishing models) and have developed many open computational tools and resources (e.g. Polygenic Score Catalog, OmicsPred, SRST, flashpca etc). We are also leaders in Sustainable Computing (e.g. Green Algorithms Project, GREENER Principles).

Project: Examples of research projects include identifying causal mediators between genetics and cardiometabolic diseases, developing new computational methods for predicting causal relationships between molecular traits, and exploring the links between genetics, biomolecular traits, and multimorbidity. Projects are co-designed by the student and supervisors.

The PhD studentship is jointly supervised by Professor Michael Inouye and Dr Scott Ritchie.

Research Environment: The PhD studentship will be hosted at the Department of Public Health and Primary Care (https://www.phpc.cam.ac.uk/ ). The student will be based at the Victor Phillip Dahdaleh Heart and Lung Research Institute (HLRI, https://www.hlri.cam.ac.uk/ ) in the BHF Cardiovascular Epidemiology Unit (CEU, https://www.phpc.cam.ac.uk/ceu/ ). The CEU is one of the world leaders in using multi-modal, high-dimensional molecular and health data to both predict risk of future disease and to understand underlying aetiology. Set within the HLRI, our unit has state-of-the-art facilities and interdisciplinary linkages with experimental and clinical researchers. Located at the centre of the Cambridge Biomedical Campus, the largest of its kind in Europe, we collaborate with neighbouring institutions including Addenbrooke's hospital, Royal Papworth hospital, MRC Laboratory for Molecular Biology, CRUK Cambridge Institute, MRC Biostatistics and other Units as well as industry partners (e.g. AstraZeneca and others).

Health Data Research UK is the national institute for data science and healthcare and provides a multi-faceted environment for PhD studies. The Molecules to Health Records Driver Programme, led by Wellcome Sanger Institute in collaboration with the University of Cambridge and the University of Oxford, provides an exciting and comprehensive programme of research which will enhance any research project.

The student will be encouraged to closely interact with the other teams within the Cardiovascular Epidemiology Unit, and to work with our academic and clinical partners to ensure their work could have real-world impact.

Value to the Student: The student will develop valuable skills in genomics, multi-omics, machine learning, epidemiology, biostatistics, data manipulation, programming in statistical software packages (e.g. in R, Python) and handling and analysis of large-scale data (including multi-omics and electronic health records).

Our strategy is to train researchers to work fluently across quantitative and clinical domains, and to have a deep appreciation of the methodological, clinical, biological, behavioural and policy issues facing biomedical data sciences. The student will benefit from opportunities to network across institutions (Cambridge, Nottingham and Oxford) and blood services (NHSBT and the Australian Red Cross Lifeblood), make connections with staff/students within other BTRUs and participate in Patient and Public Involvement and Engagement (PPIE) activities (e.g., involving the public in your research and presenting work at public events). The student will have access to courses across the partner universities, including modules on epidemiology, applied data analysis and clinical trials.

Requirements: Applicants are expected to hold (or to have achieved by the start date) at least a good upper second class (good 2:1) honours degree from a UK university or an equivalent standard from an overseas university; and preferably a Master's degree in a biological or quantitative science. For example, computer science, statistics, mathematics, epidemiology, (bio)chemistry, physics, engineering. They should have experience with programming (e.g. python, R etc) and enjoy working in a team environment.

To check if your international qualification meets the University minimum requirement, please consult the International section of our website.

Benefits: We invite applications from UK and non-UK students. Each studentship provides a stipend of £18,622 per year for 3 years. UK-level tuition fees are covered; other applicants will need to secure additional funding for overseas student fees. Please ensure you meet the University of Cambridge entrance requirements: see https://www.postgraduate.study.cam.ac.uk/application-process/entry-requirements .

Enquiries: We welcome informal discussions about this post. Please contact Professor Michael Inouye ([email protected] )

How to apply

To apply please visit https://www.postgraduate.study.cam.ac.uk/courses/directory/cvphpdhpc and click 'Apply Now'.

Course details: PhD in Public Health & Primary Care (Full-time) Start Date: October 2024 or earlier Supervisor(s): Professor Michael Inouye Research Title: PhD Studentship ¿ Biomedical Data Science

In order to apply for this opportunity, you will need:

• Details of two academic referees (references will be taken up immediately).

• Transcript(s)

• CV/resume

• Evidence of competence in English

• Statement of Interest outlining your suitability, why you are interested in a PhD in this area, your background and research interests

Interview and Selection process

The deadline for applications is 22 December 2023 Applicants will be notified of the outcome of their application by end of January 2024 Shortlisted candidates will be invited to interview on rolling review basis. Applicants will be notified of the outcome of their interview soon after.