PhD studentship Available

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Developing Artificial Intelligence Approaches to Investigate Epigenomic Regulatory Logic in Healthy and Disease States

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We are offering a fully funded PhD project focused on decoding genomic variation, TF-epigenomic dynamics and enhancer-promoter interactions that drive gene expression across diverse biomedical models, including Cancer, Aging, Inflammation & Immunity and Metabolic Disease. Leveraging the Samarajiwa Lab’s extensive expertise in applying machine and deep learning (Newsham et al, Biol. Meth. Prot., 2024; Rossi et al, Sci Adv. 2022; Martens et al, bioRxiv 2020) and in regulatory epigenomics (Patel et al, Nat. 2022; Tomimatsu et al Nat. Aging 2021; Rodriguez et al, Cancer Disc. 2018; Kirschner et al. PLoS Genet. 2015). The successful candidate will develop innovative and efficient predictive AI models, incorporating Neuro-Symbolic AI approaches,  Knowledge Graphs, logic and graph neural network approaches that will assist in identifying TF utilization patterns, delve into the consequences of genetic variants, characterize enhancer classes, predict and map functional enhancer targets, and ultimately help illuminate the fundamental rules governing enhancer target selection in both healthy and disease states.​

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Requirements: Applicants must hold (or be on track to obtain) a 2:1 (or equivalent) BSc in a relevant computational subject. A Masters degree (Computer Science, Physics, Engineering, Artificial Intelligence, Deep Learning, Mathematics, Computational Biology, Bioinformatics, or Systems Biology) is preferable but not essential. Applicants must also meet Imperial College’s English language requirements: further details can be found at https://www.imperial.ac.uk/study/pg/apply/requirements/english/.

 

While prior knowledge of biology is not required, a strong interest in applying AI based approaches to biological/ biomedical science and a willingness to learn the relevant biology required for the project is expected.

 

Prior experience in data science, Python programming and Machine/Deep Learning model development (PyTorch or Keras) is highly desirable. An interest in biomedical applications of foundation models, LLMs, DNA language models, Knowledge Graphs, Neuro Symbolic AI approaches and use of agentic AI is also desirable.

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Funding: Includes course fees at the UK-Home rate (36 months) and a tax‐free stipend (42 months) based on the annual UKRI rate of £23,805. Funding for overseas fees is not provided.

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Applicants should submit their CV and a cover letter, including full contact details of two referees, to Dr Shamith Samarajiwa (s.samarajiwa at imperial.ac.uk). Informal enquiries are welcome, please email Dr Shamith Samarajiwa if you wish to discuss the project prior to formal application. Closing date is 31st October 2026 at 24:00 GMT. Any applications received after this date/time or without all requested documentation will not be considered. Shortlisted applicants will be interviewed in November.

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