Interpretable AI for discovering structure–function rules in submerged fungal fermentation
The Faculty of Science Doctoral Training Centre (DTC) in Artificial Intelligence (AI) is a new initiative by the University of Nottingham to train future researchers and leaders to address the most pressing challenges of the 21st Century, through foundational and applied AI research on a cohort basis. The DTC training and supervision will be delivered by a team of outstanding scholars from different disciplines cutting across arts, engineering, medicine and health sciences, science, and social sciences.
Project overview
Start date: 1 October 2026 for 3.5 years (42 months)
Filamentous fungal fermentation underpins a multi-billion-pound global biomanufacturing sector spanning food, enzymes, chemicals, and alternative proteins and is expected to grow rapidly as sustainable fermentation scales. However, these systems remain difficult to optimise because fungal growth can adopt very different mycelial structures e.g. pellets, clumps, dispersed hyphae, in response to small changes in process conditions. This PhD will develop interpretable AI methods that move beyond prediction to discover mechanistic structure–function rules from multimodal datasets, enabling improved understanding and control of morphology-driven performance in fungal fermentation.
In Year 1, you will focus on laboratory experimentation, cultivating filamentous fungi under controlled conditions systematically perturbing operating parameters and collecting microscopy/morphology, sensing, and biochemical data. You will build strong foundations in machine learning through formal training courses, hands-on data analysis, and close shadowing of a senior PhD researcher to develop practical skills in data preprocessing, model development, and interpretation.
In Year 2, you will develop and refine interpretable AI models (e.g. symbolic regression and causal inference) to extract mechanistic structure–function relationships.
In Year 3, AI predictions will guide new targeted fermentation experiments and be tested through an industrial placement applying the developed framework to real fermentation processes.
This PhD uniquely equips you with both experimental lab and advanced machine learning skills, producing a rare hybrid researcher fluent in biomanufacturing and AI.
Supervisors
- Dr Oliver Fisher - Faculty of Engineering
- Dr Asma Ahmed - Faculty of Engineering
- Dr Vincenzo di Bari - School of Biosciences
Candidate requirements
A strong enthusiasm for artificial intelligence research and a minimum of a 2:1 bachelor's degree in a relevant discipline to the research topic - please consult with the potential supervisors. See the "how to apply" section for contact details.
Read our application guide for full guidance on residency, qualifications and English language requirements.
Funding and eligibility
This studentship is open to UK/home applicants only.
Annual tax-free stipend based on the UKRI rate (£21,805 for 2026/27), home tuition fee, and a £3,000 p.a. Research Training Support Grant.
How to apply
Application deadline: Sunday 19 April 2026. You must have completed and submitted your application to the NottinghamHub system by this date.
Read our application guide for full guidance on how to apply. The application process has two steps.
Email Dr Oliver Fisher (Faculty of Engineering) for further details and to arrange an interview.