Application Deadline: November 27, 2026, 5:00 PM | Interviews from November 30, 2026 | Start March 2027
| Positions | 1 fully funded PhD studentship |
| University | Teesside University – Centre for Digital Innovation |
| Location | Middlesbrough, United Kingdom |
| Funding & Salary | Tuition fees covered for up to 4 years + tax-free stipend of £21,805 per year for 3 years |
| Start Date | March 2027 |
| Application Deadline | November 27, 2026, 5:00 PM |
| How to Apply | Online application form; academic enquiries: [email protected] |
Scholarship Description:
The Centre for Digital Innovation at Teesside University, led by Professor Claudio Angione, is offering a fully funded PhD studentship on Mechanism-Aware Deep Learning for Food Fermentation. Biomanufacturing is undergoing a revolution driven by AI advances, yet existing deep learning architectures struggle with long-term temporal dependencies, sparse multimodal alignment, and mechanistic interpretability. This project develops new dynamic multimodal architectures that are aware of biological mechanisms and are trained on high-dimensional industry data.
Food industries such as Quorn use proteins produced by fungi (e.g., F. venenatum) as ingredients for meat substitutes, but food-related bioprocessing often lacks the capability to fully control and optimise the process for yield, consistency, and environmental sustainability. The goal of the project is to design a mechanism-aware deep learning architecture that integrates real-time imaging, proteomics, and fermentation data. The architecture will be tested on tasks such as predicting the final quality of the paste, detecting C-variants, and finding features and early modifications that allow optimisation of a time-resolved fermentation campaign over 30 days.
The envisaged architecture will be highly multimodal, harnessing recent advances in transformer-based architectures to integrate modalities including long-horizon time-resolved and high-resolution imaging, proteomics, and metabolic modelling data. Cross-attention layers will allow the model to learn interactions and dependencies between modalities and how their combination impacts the performance of a given campaign. The model will be collaboratively assessed and validated with Quorn using non-sensitive fermentation data, to facilitate direct industrial implementation.
Available Subjects:
- Transformer architectures and multimodal data fusion.
- Representation learning and explainable AI for bioprocessing.
- Physics-informed neural networks (PINNs) and metabolic modelling.
- Machine learning with tangible real-world sustainability impact (food fermentation, in collaboration with Quorn).
Eligibility criteria:
- Applicants should hold or expect to obtain a good honours degree (2:1 or above) in a relevant discipline.
- A master’s level qualification in a relevant discipline is desirable, but not essential, as well as a demonstrable understanding of the research area.
- Applications are welcome from UK or international students.
- International students are subject to the standard entry criteria relating to English language ability, ATAS clearance and, when relevant, UK visa requirements.
- The project is looking for candidates with knowledge or strong interest in transformer architectures, multimodal data fusion, representation learning or physics-informed neural networks (PINNs), with a passion for solving algorithmic problems with tangible real-world sustainability impact.
Application Procedure:
Apply online using the Online Application (Funded PhD) application form. When asked to specify funding, select “other” and enter RDS and the title of the PhD project (Mechanism-Aware Deep Learning for Food Fermentation). Clearly indicate that you are applying for a Funded Studentship and state the project title on the research proposal you upload.
All applications received by the closing date will be considered; shortlisted applicants will be contacted for an online interview (interviews from November 30, 2026). Academic enquiries: [email protected]; administrative enquiries: [email protected].
Official studentship page: https://www.tees.ac.uk/sections/research/funding_details.cfm?fundingID=312
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