PhD Studentship: Generative and Hybrid AI for Pharmaceutical Reactor Design

Application deadline: 30/06/2026
Research theme: Bayesian Models, Machine Learning, Generative Models, Physics-Informed Machine Learning
How to apply: https://uom.link/pgr-apply-2425

This 3.5-year PhD project is fully funded and home students are eligible to apply (we also welcome students from the Republic of Ireland and EU students with settled status). The successful candidate will receive an annual tax-free stipend set at the UKRI rate (£21,805 for 2026/27) and tuition fees will be paid. We expect the stipend to increase each year. The start date is October 2026.

We recommend that you apply early as the advert may be removed before the deadline.

This project will develop an integrated AI framework to design crystallisation reactors more effectively. It will first use generative AI to create and explore new reactor shapes. A Variational Autoencoder (VAE) will encode complex 3D reactor cross-sections into a continuous latent design space. It will then build hybrid models of crystallisation dynamics, combining physical understanding with data-driven reaction kinetics, so that reactor performance can be predicted more quickly than with full simulations alone. Finally, it will use uncertainty-aware optimisation to identify reactor designs and operating conditions that are not only high-performing, but also reliable.

By combining expertise in reactor design and optimisation, hybrid modelling of complex process systems, and generative machine learning, the project will create a new route for discovering improved crystallisation reactors for pharmaceutical manufacturing.

Applicants should hold (or be about to obtain) a First or Upper Second class (2:1) UK honours degree (or international equivalent) in a relevant subject such as in a relevant discipline such as Chemical Engineering, Mechanical Engineering, Aerospace Engineering, Physics, Mathematics, Computing, Data Science or a closely related subject.

We strongly recommend that you contact the lead supervisor for this project, Dr Nausheen Basha (nausheen.basha@manchester.ac.uk), before you apply. Please include details of your current level of study, academic background, and any relevant experience (including your Github page, if any), together with a cover letter explaining your motivation to undertake this PhD project. It is essential that you also include a copy of your CV and transcripts. Please include the project title and your name in the subject line of your email.

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