Funded PhD Opportunity in Mechanistic and Interpretable AI for Personalised Eczema Severity Forecasting

AI for Healthcare & Computational Systems Medicine

Join us to do your PhD at the intersection of AI, biology, and digital health to make personalised eczema forecasting a clinical reality

PhD Opportunity at AI for Healthcare Centres & Tanaka Group, in collaboration with Pierre Fabre Laboratoires, starting October 2026

Are you passionate about applying cutting-edge machine learning to address the challenges of allergic skin disease? The AI for Healthcare Programme & The Tanaka Group at Imperial College London are seeking a highly motivated PhD researcher to drive research with the potential to transform how patients manage eczema, empower them to anticipate symptom changes, understand the biological drivers behind their condition, and make informed treatment decisions through a smartphone app.

This is a unique opportunity to conduct impactful, translational research at the intersection of AI, bioengineering, and clinical medicine, with direct collaboration with a leading international pharmaceutical company.

The Project

Eczema is the most common allergic skin disease, characterised by unpredictable fluctuations in symptom severity that significantly impact patients’ quality of life. Currently, no tools exist to forecast changes in eczema severity at the individual patient level.

This project aims to develop mechanistic and interpretable AI tools for personalised eczema severity forecasting by integrating smartphone images of affected skin, patient-reported severity scores and outcomes, and skin barrier and microbiome measurements. By embedding disease mechanisms within a Bayesian modelling framework, the AI tool will generate predictions explicitly linked to underlying biological drivers, transforming black-box forecasts into clinically actionable, biologically grounded risk assessments. This approach enables explainable predictions that are meaningful to both patients and clinicians, supporting more personalised and informed disease management.

The Supervisory Team

You will be supervised by an interdisciplinary team with complementary expertise spanning AI, clinical medicine, and industry.

·      AI supervisor: Professor Reiko Tanaka, Department of Bioengineering https://www.rtanakagroup.com/        

·      Clinical supervisor: Professor Adnan Custovic, National Heart and Lung Institute (NHLI) https://profiles.imperial.ac.uk/a.custovic

·      Industry supervisor: Dr Gwendal Josse, Pierre Fabre Laboratories https://www.pierre-fabre.com/en

What background should you have

We are looking for a highly motivated researcher who thrives in a collaborative, interdisciplinary environment and is eager to engage with diverse scientific perspectives. The ideal candidate will demonstrate

·      A strong Master’s degree in Mathematics, Statistics, Machine Learning, Engineering, Computer Science, or a closely related discipline

·      Excellent written and oral communication skills

·      Strong interpersonal skills and enthusiasm for working across academic and industry settings

·      A genuine interest in applying AI to real-world healthcare challenges 

More details (Requirements and Application process) and Contact

Please visit our AI4Health webpage https://ai4health.io/training/ for more information about the entry requirements and application process to the AI4Health Programme, and the Tanaka Group website https://www.rtanakagroup.com/ to learn more about the research domain.

Please get in touch with your queries at ai4health-admissions@imperial.ac.uk

Funding Notes

This PhD opportunity comes fully funded for Home (UK) students ONLY. The studentship covers tuition fees (£5238 pa in 2026-27) and pays a bursary of £23,805 pa, but can be up to £31,000 pa*, for at least three years and a maximum of 4 years. There is also funding support to travel to leading AI conferences such as ICML, AISTATS and MLHC. If opting to take part in the TechExpert pilot*, the studentship would pay an enhanced stipend of £31,000 per annum for doctoral students with Home/UK fee status ONLY.