Fermentelligence: AI Enabled Valorisation of Agri Food Side Streams into Microbial Protien

One full scholarship is available in the School of Food Science and Nutrition in 2025. This scholarship is open to UK applicants and covers UK tuition fees <br /> <br /> This fully funded PhD place provides an exciting opportunity to pursue postgraduate research in a range of fields relating to Food Science, Bioprocessing, Alternative Proteins and Data Analytics and is aligned to the UK’s National Alternative Protein Innovation Centre (National Alternative Protein Innovation Centre (NAPIC) - NAPIC) and The Food AI Labs (Food AI Lab) Bezos Earth Fund project to develop and AI platform to upcycle agri-food side streams. <br /> <br /> The award is open to full-time candidates who have been offered a place on a PhD degree at the School of Food Science and Nutrition.

<p>Global demand for animal-sourced protein is projected to double by 2050. Complementary protein sources with enhanced sustainability are urgently needed to ensure global protein security while reducing environmental pressures such as deforestation driven by livestock farming. Microbial fermentation offers a powerful alternative, producing high protein biomass as well as animal protein equivalents via precision fermentation. Critically, fermenting agri food side streams, such as brewers spent grain, bread waste, and surplus horticultural crops, instead of high value sugar crops can dramatically improve the economic and environmental viability of microbial protein, transforming today's waste into tomorrow's food. </p> <p>This PhD will be based in the <a href="https://foodailab.co.uk/">Food AI Lab</a> and utilise the school's brand-new Digital Fermentation Studio, a state of the art facility funded by BBSRC through NAPIC, the National Alternative Protein Innovation Centre that combines instrumented bioreactors with an integrated suite of advanced sensors. The student will run fermentations on a range of agri food side streams and tackle two interlinked research questions at the frontier of bioprocess science and engineering. First, how can we exploit multi modal sensor data, including near infrared spectroscopy, ultrasonic sensors, dielectric and capacitance probes, and off gas analysis, together with machine learning, to predict key biomass parameters in real time? Targets will include cell concentration and viability, protein content, substrate utilisation, and metabolite profiles, replacing slow offline assays with inline, real time insight. Second, how can these predictive models be coupled with optimisation algorithms to drive fermentations toward maximum protein yield and quality while simultaneously accounting for economic factors such as substrate cost, energy use, productivity and environmental factors such as carbon footprint, water use, side-stream valorisation? The result will be a new framework for multi objective, AI guided fermentation tailored to the messy, variable nature of real agri food side streams. </p> <p>Specifically, this project will include: </p> <ul> <li>Designing and running fermentation experiments on diverse agri food side streams in the Digital Fermentation Studio </li> <li>Multi sensor data acquisition, fusion, and processing across complex, heterogeneous substrates </li> <li>Developing machine learning models for real time prediction of biomass and protein parameters, integrated where appropriate with metabolic modelling </li> <li>Closed loop optimisation of fermentation conditions for yield, economic performance, and environmental impact </li> <li>Techno economic and life cycle analysis to benchmark the most promising side streams and process strategies </li> </ul> <p>The successful candidate will have the opportunity to engage with the world leading experts and centres in AI and alternative protein and interact with global industry and academic partners.   </p> <p> </p> <p><strong>Selection Process </strong></p> <ul> <li>All applications will be considered after the deadline. Only complete applications will be considered.</li> <li>Awards are based on academic merit and no other factors such as financial hardship are taken into account.</li> <li>Applicants will be e-mailed within 2 weeks of the deadline to confirm the outcome.</li> </ul> <p><br /> <strong>Other Conditions</strong></p> <ul> <li>Awards must be taken up by 1 October 2026</li> <li>Candidates who have previously been awarded a PhD or are currently registered on a PhD are excluded from applying. Those who were previously studying for a PhD but did not complete may be considered</li> <li>The awards are available for new Postgraduate Researchers undertaking full-time research study leading to the degree of PhD. </li> <li>Applicants must live within a reasonable distance of the University of Leeds whilst in receipt of this Studentship/ scholarship.</li> <li>Please be aware that any expenses related to the relocation of international students to the UK (visa, insurance, NHS fees, flights, etc) would be their responsibility and is not covered by this award.<br />  </li> </ul>

<p>To apply for this project you will need to make a formal application for research degree study through <a href="https://www.leeds.ac.uk/research-applying/doc/applying-research-degrees">the University's website</a>. You will need to create a login ID with a username and PIN. </p> <p>•    For Application type please select Research Degrees, Research Postgraduate. <br /> •    The admission year for this project is 2026/2027 Academic Year. <br /> •    You will need to select your Planned Course of Study from a drop-down menu. For this project, scroll down and select PhD Food Science Full-time. <br /> •    The project start date for this project is 1 October 2026, please use this as your Proposed Start Date of Research. <br /> •    Please state clearly in the research information section that the research degree you wish to be considered for is Fermentelligence: AI-Enabled Valorisation of Agri-Food Side Streams into Microbial Protein as well as Nicholas Watson as your proposed supervisor.</p> <p>More information on how to apply is available on our website <a href="https://environment.leeds.ac.uk/food-nutrition-research-degrees">Research degrees | School of Food Science and Nutrition | University of Leeds.</a> <br />  </p> <p paraeid="{c7e02acb-c8c5-4f68-b956-ce5f27c79879}{77}" paraid="1231857984">If English is not your first language, you must provide evidence that you meet the University's minimum English language requirements (below). </p> <p><em>As an international research-intensive university, we welcome students from all walks of life and from across the world. We foster an inclusive environment where all can flourish and prosper, and we are proud of our strong commitment to student education. Across all Faculties we are dedicated to diversifying our community and we welcome the unique contributions that individuals can bring, and particularly encourage applications from, but not limited to Black, Asian, people who belong to a minority ethnic community, people who identify as LGBT+ and people with disabilities. Applicants will always be selected based on merit and ability.</em></p>

The minimum entry requirements for PhD study is a 2.1 honours Bachelor degree, or equivalent, in a subject relating to your proposed area of research, or a good performance in a Master’s level course in a relevant subject.<br /> <br /> Applicants who are uncertain about the requirements for a particular research degree are advised to contact the PGR Admissions Team (env-pgr@leeds.ac.uk) prior to making an application.<br />

The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.0 overall with at least 5.5 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid.

<p>We are offering a fully funded scholarship to study the project Fermentelligence: AI-Enabled Valorisation of Agri-Food Side Streams into Microbial Protein, at the School of Food Science and Nutrition, University of Leeds for one UK status candidate. The funding covers UK tuition fees as well as a UKRI matched maintenance stipend (currently £20,780 in 2025/26) per year, for three  and a half years, subject to satisfactory progress.</p> <p><strong>Eligibility Criteria </strong><br /> •    Applicants must be eligible to pay fees at the Home (UK) rate.</p> <p>If you are unsure whether you are eligible for UK fees/funding, please see our <a href="https://www.leeds.ac.uk/undergraduate-fees/doc/fee-assessment">fee assessment page.</a><br />  </p>

<p>For further information please contact Prof Nik Watson (<a href="http://N.J.Watson@leeds.ac.uk">n.j.watson@leeds.ac.uk</a>)</p>