Research

Current studentships

Mechanism-Aware Deep Learning for Food Fermentation

Deadline: 27/11/2026 5:00PM
Studentship code:
Staff name: Professor Claudio Angione
Research centre: Centre for Digital Innovation
Start date: Successful applicants will start in March 2027.

Biomanufacturing is currently 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 PhD project aims to address these fundamental AI challenges by developing new dynamic multimodal architectures that are aware of biological mechanism and are trained on high-dimensional industry data.

Food industries, such as Quorn, use proteins produced by fungi, such as F. venenatum, as ingredients for meat substitutes. However, food-related bioprocessing often lacks the capabilities to fully control and optimise the process for yield, consistency, and environmental sustainability. State-of-the-art explainable AI techniques based on multimodal transformers and physics-informed neural networks (PINNs) have the potential to address these issues, but they natively lack the mechanistic interpretability crucial for robust industrial applications.

The goal of this project is to design a mechanism-aware deep learning-based architecture that integrates real-time imaging, proteomics, and fermentation data. The architecture will be tested on several tasks, for instance, to predict the final quality of the paste, detect C-variants, and find features and early modifications that would allow optimisation of a time-resolved fermentation campaign over 30 days.

The envisaged deep learning architecture will be highly multimodal, harnessing recent advances in transformer-based architectures to integrate various 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 them, and how their combination impacts the performance of a given campaign. The combined features will yield a unified representation of the fermentation state, also incorporating mechanistic knowledge at the metabolic level.

We are 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.

The deep learning model will be collaboratively assessed and validated with Quorn. Non-sensitive fermentation data, and established fermentation assessment methodologies will be employed to confirm the biological relevance of the findings, to facilitate direct industrial implementation.

Funding eligibility

This fully funded PhD Studentship covers tuition fees for the period of a full-time PhD Registration of up to four years and provide an annual tax-free stipend of £21,805 for three years, subject to satisfactory progress.

Applications are welcome from UK or international students.

Entry requirements

Applicants should hold or expect to obtain a good honours degree (2:1 or above) in a relevant discipline.

A masters level qualification in a relevant discipline is desirable, but not essential, as well as a demonstrable understanding of the research area. Further details of the expected background may appear in the specific project details.

International students will be subject to the standard entry criteria relating to English language ability, ATAS clearance and, when relevant, UK visa requirements and procedures.

How to apply

Applicants should apply online for this opportunity 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 that you are applying for.

You should ensure that you clearly indicate that you are applying for a Funded Studentship and the title of the topic or project on the proposal that you will need to upload when applying.

To apply for more than one project, you need to complete a further application form and specify the relevant title for each application to a topic or project.

Applications for studentships that do not clearly indicate that the application is for a Funded Studentship and state the title of the project applied for on the proposal may mean that your application may not be considered for the appropriate funding.

Apply now

Selection process

All applications received by the closing date will be considered. Successful applicants at the application stage will be shortlisted and contacted to arrange an interview. All interviews will be held online. Unsuccessful applicants will be contacted to confirm that the application will not be progressed. After interview, all interviewed applicants will be contacted to inform them of the outcome. Successful applicants progressing to an offer of a place, to commence in March 2027.

About Teesside

We are committed to providing a safe, welcoming and inclusive campus and to supporting all members of our University community to thrive whatever their age, gender, disability, sexual orientation, gender identity, race, marital status, nationality or any other characteristic.
More about our Inclusive campus

As a Teesside University research student, you will join a growing and dynamic research community, allowing you to share your experiences, insight and inspiration with fellow researchers. You will benefit from our academic expertise and be supported through a strong programme of research training. You will be offered opportunities and support at each stage of your research degree. Our research is designed to have impact, and to influence policy and practice within our region, the UK and beyond. We work with external organisations to anticipate and respond to research needs, and to put our research into practice in sectors as diverse as the arts, engineering, healthcare and computing.

PhD students are encouraged to work with their supervisors to explore the potential impact of their work.

The successful candidate will be expected to participate fully in research group and centre activities, including training sessions and workshops, and will become a member of the University's wider postgraduate research community. Mentoring and support will be provided for the development of a strong academic and professional CV during the PhD.

Academic enquiries

For academic enquiries, please contact c.angione@tees.ac.uk.

For administrative enquiries before or when making your application, contact research.enquiries@tees.ac.uk.

After an application has been made, please contact research.admissions@tees.ac.uk.

Key dates

Deadline: 27/11/2026 5:00PM
Interview date: Interviews will take place from 30/11/2026.
Start date: Successful applicants will start in March 2027.