Deep Learning Models for Dissolution Dynamics of Amorphous Formulations in Patient-Specific Gastrointestinal Geometry

This PhD studentship at Imperial College London offers an exciting opportunity to advance the prediction of oral drug performance using deep learning and computational fluid dynamics (CFD). Current tools, such as the TIM-1 system and CFD models, provide valuable insights under controlled in vitro conditions but often fail to capture the variability in gastric geometry and motility across patients. This project aims to address these limitations by developing geometry-conditioned neural operators that learn from CFD simulations of drug dissolution and precipitation in patient-specific gastrointestinal geometries. By combining physics-based simulation with deep learning, the research will enable accurate, patient-level prediction of oral drug performance, particularly for complex formulations like amorphous solid dispersions. The project is part of CEDAR, an EPSRC Centre for Doctoral Training (CDT) in cyber-physical systems for medicines manufacturing, offering a unique environment for collaboration with industry leaders and access to cutting-edge resources. PhD researchers will bridge AI, computational fluid dynamics, and pharmaceutical science to revolutionise how oral drug performance is predicted. You will develop geometry-conditioned deep learning models that learn how drug dissolution and precipitation behave inside patient-specific gastrointestinal geometries derived from MRI or CT data. The integration of machine learning with physics-based simulations will facilitate the creation of fast, patient-centric models for drug dissolution dynamics, contributing to the advancement of personalised medicine and pharma innovation. The studentship covers home tuition fees, research/training costs, and provides a monthly stipend for 4 years (minimum annual stipend of £20,780, tax-free). Part-time opportunities may be available; interested candidates should contact [email protected] for more information. Entry requirements include a minimum of a 2:1 Honours degree (or international equivalent) in chemical engineering, chemistry, computer science, data science, electrical engineering, materials science, mechanical engineering, pharmaceutical sciences, physics, or a relevant science or engineering discipline. An MSc is desirable. International students whose first language is not English must have an IELTS score of 6.5 (with no less than 5.5 in any element). To apply, visit the FindAPhD project link and follow the instructions for the EPSRC CDT in Cyber-Physical Systems for Medicines Manufacturing. Ensure you meet the eligibility criteria and contact the provided email for further information about part-time options.