O Matar

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.

This PhD project at Imperial College London focuses on developing deep learning models for predicting the dissolution dynamics of amorphous drug formulations within patient-specific gastrointestinal geometries. Current tools, such as the TIM-1 system and computational fluid dynamics (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 limitation leads to predictions that do not accurately reflect in vivo drug performance, especially for complex pharmaceutical formulations like amorphous solid dispersions. The research aims to create geometry-conditioned neural operators that learn from CFD simulations of drug dissolution and precipitation in patient-specific gastrointestinal geometries derived from MRI or CT data. By integrating physics-based simulation with advanced deep learning techniques, the project will enable accurate, patient-level prediction of oral drug performance. This approach bridges artificial intelligence, computational fluid dynamics, and pharmaceutical science, offering a novel framework for personalised medicine and digital manufacturing processes. This PhD studentship is part of CEDAR, an EPSRC Centre for Doctoral Training (CDT) in cyber-physical systems for medicines manufacturing. The CEDAR programme is an 8.5-year initiative that provides PhD researchers with unique opportunities to collaborate with industry leaders and build comprehensive toolkits for advanced medicine manufacturing. The project offers exposure to cutting-edge research in pharma innovation, geometry-aware AI, and personalised medicine. Funding is available for home tuition fees, research and training costs, and a monthly stipend for four years (minimum annual stipend of £20,780, tax-free). Part-time opportunities may be available; interested candidates should contact [email protected] for more information. Applicants must hold at least 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 applicants whose first language is not English must provide an IELTS score of 6.5 (with no less than 5.5 in any element). The application deadline is July 31, 2026. To apply, visit the provided application link and follow the instructions for the EPSRC CDT in Cyber-Physical Systems for Medicines Manufacturing. For further information or to inquire about part-time options, contact [email protected].