[School of Engineering PhD Scholarships] Physics-Grounded Learning of AI Agents for Designing Vaccine Reactors

About the project

About the Project Artificial intelligence is increasingly used in engineering design, but researchers still decide which simulations to run, whether results are physically credible, and what to investigate next. This PhD asks: can physics feedback teach AI agents to make better scientific decisions? This PhD will develop physics-grounded AI agents for designing reactors used to manufacture lipid nanoparticles (LNPs) for vaccines and therapeutic delivery. It builds on our recent Nature Chemical Engineering work on AI-guided reactor design and our ICML AI4Physics work on machine-learning surrogates for complex flows. Using existing CFD and surrogate models, the student will train open-weight AI agents to learn from physics-based feedback. The agent will decide when to explore a new design, run a simulation, increase model fidelity, query a fast surrogate, or reject an unphysical solution. The project will investigate verifier-guided self-correction, learning from successful and failed computational trajectories, and reinforcement learning from physics feedback. The ambition is to show that physics can become a training signal for scientific AI, enabling agents that make increasingly reliable engineering decisions. The project is in collaboration with Professor Jayne Lawrence MBE, Director of the North West Centre for Advanced Drug Delivery, bringing leading expertise in nanomedicine and lipid-based drug delivery. This project is expected to start in September 2027. Before you apply: We strongly recommend that you contact the supervisors for this project before you apply. How to apply: To be considered for this project you must complete a formal application through our online application portal. If you already have an applicant account this link will directly open an application for PhD School of Engineering Scholarships . If you don’t already have an applicant account, please follow the instructions here. . When applying, please specify the full title and supervisor/s of the project, details of your previous study, and names and contact details of two referees. You must also upload a Supporting Statement describing the motivation to apply to the project, your CV and transcripts of awarded and in-progress university qualifications . Please note late or incomplete applications will not be considered. Equality, diversity and inclusion are fundamental to the success of The University of Manchester and central to all our activities. A diverse research community strengthens creativity, productivity and quality, while increasing the societal and economic impact of our work. We welcome applicants from all career paths, backgrounds and sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation or transgender status. We welcome applications from candidates returning to study after a career break or experience in other roles. Flexible study arrangements may be available, including part-time study at 50%, 60% or 80%, subject to the requirements of the project and funder. Eligibility : The standard academic entry requirement for this PhD is an upper second-class (2:1) honours degree in a discipline directly relevant to the PhD Chemical Engineering, Mechanical Engineering, Applied Mathematics, Computer Science, Physics(or international equivalent) OR any upper-second class (2:1) honours degree and a Master’s degree at merit in a discipline directly relevant to the PhD Chemical Engineering, Mechanical Engineering, Applied Mathematics, Computer Science, Physics (or international equivalent). Previous experience in computational modelling, CFD, machine learning, optimisation or scientific programming is desirable. This project will remain open until filled. If your application is submitted by 1 st November 2026, you can expect a decision by 18 th December 2026. If your application is submitted by 15 th January 2027, you can expect a decision by 30 th March 2027. Self or externally funded students can also be considered for this project. FSESoE

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