Overview
Location: The Discovery Centre, Cambridge Biomedical Campus, Cambridge, UK
Salary: £40,000 gross (subject to deductions in line with UK policy) plus benefit fund and bonus.
AstraZeneca UK has received funding from the Marie Skłodowska‑Curie Actions programme through the EU and is now pleased to offer this position.
Project: 101226456 — MLCARE — HORIZON-MSCA-2024-DN-01 1 DC 12 MSCA Doctoral Network MLCARE (Machine Learning Computational Advancements for personalized Medicine)
Cracking the genetic code of weight management with AI. Weight management is a complex challenge – what if AI could help us understand who is at risk of weight‑related issues, why, and what interventions would be most effective?
This PhD project will harness deep learning and multi‑omics to uncover the hidden genetic and biological drivers of weight regulation. By enhancing genome‑wide association studies (GWAS) with cutting‑edge machine learning, the project aims to identify genetic variants and effector transcripts that influence body weight, metabolism, and individual responses to treatment. The models will integrate genomic insights with behavioural and clinical data to develop personalised, precision‑driven strategies—redefining how weight is monitored, managed, and improved over time.
Secondments
* AstraZeneca España – AZ (Centre for Artificial Intelligence) (ES): developing big‑data methods for enhanced GWAS with omics (potentially June – Aug. 2027).
* University of Copenhagen – UCPH (Section for Computational and RNA Biology) (DK): incorporate omic feature models into enhanced GWAS (potentially June – Aug. 2028).
* Institut Pasteur – IP (Computational Biology, Statistical Genetics group) (FR): multi‑trait obesity GWAS (March – May 2029).
Supervisors
* Dr Tom Diethe (AstraZeneca UK)
* Dr Dimitrios Athanasakis (AstraZeneca España)
* Dr Pablo M. Olmos (Universidad Carlos III de Madrid - UC3M)
* Dr Ole Winther (University of Copenhagen)
* Dr Hanna Julienne (Institut Pasteur)
Project Objectives and Tasks
* Build biologically inspired, hierarchical discrete deep generative models to integrate multi‑omics with behavioural and clinical data for weight regulation.
* Enhance GWAS with deep learning to identify causal variants, effector transcripts, and pathways affecting body weight, metabolic rate, adiposity, and treatment response.
* Incorporate pathway‑based priors, regulatory networks, and tissue‑specific annotations into modelling for interpretability and robustness.
* Develop uncertainty‑aware inference, quantisation, and error‑correcting strategies to manage missingness, heterogeneity, and batch effects across data sources.
* Construct multi‑domain foundation models for behavioural data (sleep, mobility, smartphone usage) and EHR, with multi‑modal tokenisation and autoregressive/multi‑resolution backbones.
* Detect behavioural and biological change‑points signalling risk of weight‑related deterioration, relapse after weight‑loss interventions, or metabolic decompensation.
* Validate models in clinical settings and independent cohorts; derive personalised risk scores and adaptive intervention policies for weight management.
* Collaborate within a multidisciplinary network of machine learning researchers, bioinformaticians, endocrinologists, psychiatrists, and industry partners.
* Expected outcomes: methods for learning hierarchical deep generative models that fuse GWAS/TWAS with multi‑omics and behavioural data to produce interpretable embeddings and causal signals; identification of genetic variants, effector transcripts, and pathways linked to body weight regulation and differential treatment outcomes; a behavioural foundation model and change‑point detection framework for early warning of weight‑related relapse or metabolic complications; personalised strategies for precision weight management.
Essential Criteria
* Undergraduate degree in Computer Science, Mathematics, Physics, or a related quantitative field.
* Master’s degree in AI or Machine Learning within Biology is preferred but not essential.
* Minimum of 300 ECTS credits at the time of application.
Candidates Must
* Be a doctoral candidate at the date of recruitment (i.e., not already in possession of a doctoral degree).
* Be formally admitted to a PhD programme leading to a degree in at least one EU Member State or Horizon Europe associated country.
* Meet national requirements for doctoral enrolment in the host country and provide proof of admission prior to the start of the contract.
* For DC12, enrol in the UC3M Doctoral Programme (Signal Processing and Communications Engineering or Biomedical Science and Technology).
* Not have resided or carried out their main activity (work, studies, etc.) in the UK for more than 12 months in the 36 months immediately before the recruitment date (unless as part of compulsory national service or refugee status procedures).
* Be working exclusively for the action.
Preferred Starting Date
June – September 2026.
Application Information
Applicants are requested to provide a detailed CV, cover letter, academic records, proof of English proficiency, and at least two letters of recommendation.
EEO Statement
AstraZeneca welcomes and considers applications from all qualified candidates, regardless of characteristics. We offer reasonable adjustments to help all candidates perform at their best.
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