AZ funded non-clinical PhD Studentship (Fixed Term)

University of Cambridge
Cambridge
1 year ago
Applications closed

AZ funded non-clinical PhD Studentship (Fixed Term) in Partitioning the genetic effects of obesity on future comorbidity risks

Applications are invited for 4-year PhD studentship (starting October 2025) based in the University of Cambridge's Cardiovascular Epidemiology Unit and the new AstraZeneca Discovery Centre at Cambridge. The student will be working on a collaborative project entitled "Partitioning the effects of high body mass index on risk of future comorbidities", jointly supervised by Dr Samuel Lambert (Assistant Professor of Health Data Science, University of Cambridge) and Dr Xiao Jiang (Senior Data Scientist, Centre for Genomics Research, AstraZeneca).

High body mass index (BMI) is a highly prevalent modifiable risk factor for many cardiometabolic and renal diseases, but also multiple cancers and neurodegenerative disorders. BMI remains a crude measure of risk as not all people with high BMI get all possible comorbidities. This motivates research into the possibility of predicting which specific comorbidities of high BMI (obesity) an individual is most likely to develop.

The project will harness large-scale human genetic resources with deep phenotyping data (e.g., UK Biobank, INTERVAL, BELIEVE) to interrogate the molecular aetiology of BMI and identify discrete axes of risk that may inform more personalized medicine and identify therapeutic targets. The student will employ genomic and computational approaches (e.g. GWAS, polygenic scores, Mendelian randomization, machine learning methods) integrating genetic data, health records, and multi-omics data from blood.

CandidateApplicants should hold (or have achieved by the start date) a first or upper second-class degree from a UK university, or an equivalent standard from an overseas university, and preferably a Master's degree (or equivalent), in a relevant subject (e.g. epidemiology, (bio)statistics, genomics, bioinformatics). They should have strong quantitative skills, experience with programming (e.g., R, Python, etc), and enjoy working in a team environment.

The position is open to UK citizens or overseas students who meet the UK residency requirements (home fees). International students who are able to cover the additional costs of overseas tuition fees through scholarships or funding schemes will also be considered (self-funding is not permitted).

See for full details of entrance requirements and additional funding sources.

FundingFull funding covering the University Composition Fee (home tuition and college fees) and a stipend of £21,500 per annum are provided for the 4-year studentship.

Research environment and trainingThe student will primarily be based in the BHF Cardiovascular Epidemiology Unit (CEU, ) at the Victor Phillip Dahdaleh Heart and Lung Research Institute (HLRI, ). The HLRI is across the road from AstraZeneca, allowing for work across both sites. The student will benefit from a collaborative environment that promotes equality, diversity and inclusion, and values all individuals. There are no coursework requirements, but taught modules and lectures in relevant subjects are available within the CEU and wider University.

Course details: PhD in Public Health & Primary Care (Full-time) Start Date: October 2025Supervisor(s): Dr Samuel LambertResearch Title: AZ funded non-clinical PhD Studentship ¿ Obesity and comorbiditiesReference: RH43284

Two academic references. 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 processDeadline for applications is 20th November 2024Shortlisted candidates will be invited to interview week commencing 2nd December 2024, and be notified of the outcome soon after.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.The University has a responsibility to ensure that all employees are eligible to live and work in the UK.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

The University has a responsibility to ensure that all employees are eligible to live and work in the UK.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.