Key components of the project:
- AI‑powered biological discovery
- Integrating multi‑omics datasets, protein–protein interaction networks, and cross‑species longevity signatures.
- Perform experimental validation in Drosophila, including gene knockdown/overexpression, lifespan assays, and phenotypic analysis.
- Conformal prediction for trustworthy genomic inference
- Experimental lifespan analysis
- Translational ageing research
You will develop computational tools that can scan the genome for hidden longevity signatures, and you will test those predictions through targeted genetic interventions in flies. The ultimate goal: to map the genetic architecture of lifespan with high precision.
This project offers the rare opportunity to combine AI‑driven discovery with hands‑on experimental validation, contributing to one of the most exciting areas of modern biology. Interested candidates should send a CV, transcript, and a brief statement of research interests.
The student will be jointly supervised by Dr Hrvoje Augustin, whose laboratory studies the genetic, metabolic, and pharmacological modulation of lifespan and neuronal function during ageing in Drosophila and Professor Alexander Gammerman, co-inventor of conformal prediction and founding director of Royal Holloway's Centre for Reliable Machine Learning, whose work attaches calibrated confidence measures to machine learning predictions and has been applied extensively to biological and biomedical data.
Entry Requirements
The project is flexible and designed to be shaped around the successful applicant. It is particularly suited to students with a computational, mathematical, or machine-learning background who wish to apply their expertise to a high-impact problem in the biology of ageing, as well as ambitious students with a strong biological background who are keen to develop computational and data-analysis skills. The balance between computational and experimental work will be tailored accordingly, with full training provided in areas that are new to the student.
Contact
Prospective applicants should contact Dr Augustin (hrvoje.augustin@rhul.ac.uk).
