Research Studentships

PhD Studentship

Unveiling the natural sulphur potential of biological nitrogen fixation in faba bean

Supervisory Team: Dr Ravi Valluru

This PhD aims to understand the potential nature of sulphur on biological nitrogen fixation in faba beans. Sulphur is an essential mineral nutrient for plant growth and development; important for primary and specialized plant metabolites that are crucial for biotic and abiotic interactions. Sulphur also plays a crucial role in biological nitrogen fixation, a natural process belonging to legumes that symbiosis with bacteria through root nodules to fix atmospheric nitrogen. Root nodules have a high demand for sulphur. In nodulated legumes, biological nitrogen fixation is more sensitive to sulphur deficiency than nitrate uptake. However, the natural sources of gypsum-mine-derived sulphur-containing by-product’s effect on plant growth, development and biological nitrogen fixation is largely unexplored. This 3.5-year project will unveil the functional relationship between sulphur nutrition and biological nitrogen fixation in faba beans using 15N stable isotopes.

Training and Development

The successful candidate will receive comprehensive research training including technical, personal, and professional skills. All researchers at University of Lincoln are part of the Doctoral College and College of Health and Science wider postgraduate community, which provides support with high quality training and career development opportunities.

Funding

This PhD studentship is fully funded and covers University fees for 3.5 years.

Entry requirements for applicants to PhD

A Master’s (honours) degree in a relevant discipline/subject area with a minimum mark of 60% in the project element (or equivalent), and the potential to engage in innovative research, and to complete the PhD within 3.5 years.

How to Apply

To find out more about the project, please contact Dr Ravi Valluru (rvalluru@lincoln.ac.uk).

Applications will require a two-page CV and a 2000-word supporting statement, showing how the applicant's expertise and interests are relevant to the project. 

Automated Robotic Rapid evaporative ionisation mass spectrometry Meat Sampling

Supervisory team: Dr Athanasios Polydoros, University of Lincoln; Dr Nick Birse, Queen's University Belfast; Professor Mark Swainson, University of Lincoln.

Meat remains a primary source of protein for much of the public, but as the public becomes increasingly aware of the environmental impacts around meat production, consumers are looking for meat that is not only of high quality but also has lower environmental impacts and higher ethical standards. Therefore, automated quality control is of high importance for meeting consumers’ requirements while minimising the environmental impact and maximising production.

Therefore, the project Automated Robotic Rapid Evaporative Ionization Mass Spectrometry Meat Sampling (AR2MS), will develop and apply Machine Learning methods for computer vision, robot control and analysis of rapid mass spectrometry data to instantly deliver assessments on the meat quality, geographical origin, and breed. This project will deliver, in combination with a major meat producer and a provider of scientific testing solutions, a commercially viable system suitable for deployment in the business of that major meat producer. Therefore, the key research areas of the project are on Machine learning methods for robot perception and control, AI data analysis and decision-making from rapid mass spectrometry data.

During the project, the successful candidate will have close collaboration with a multidisciplinary team of experts in AI, robotics, mass spectrometry and food manufacturing. Moreover, they will collaborate with the R&D team of Cranswick, a major meat producer, and they will be supported by the scientific testing provider in the development of the computational modelling needed to deliver a proof of concept for the quality control system. The successful candidate is expected to have experience in machine learning with additional experience in robotics and/or rapid mass spectrometry considered as a plus. The candidate will gain skills in computer vision, robotics, digital modelling, data management, systems integration and to a lesser extent, exposure to analytical systems used in food production systems. 

Eligibility: Due to funder restrictions, this project is currently accepting applications from home applicants only.

Application Deadline: 12pm on Friday 2 May 2025

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Trade-offs in land use: Utilising argumentation theory and dialogue to optimise for net-benefits

Supervisory team: Dr Daniel Magnone, University of Lincoln; Professor Nir Oren, University of Aberdeen; Professor Nir Oren, University of Aberdeen

The UK’s land use can be considered a complex, multiobjective optimisation problem, balancing economic yield, biodiversity enhancement, and carbon storage. With the recent introduction of the Environmental Land Management Scheme (ELMS), the need for a decisionsupport system to help stakeholders understand the impacts and trade-offs of land parcel allocation is clear and timely. 

This PhD project will utilise advances in formal argumentation theory and dialogue to encode, reason, and explain the behaviour of multi-objective optimisation algorithms. The research will focus on optimising carbon storage, biodiversity enhancement, and economic yield from agriculture. The project aims to develop an optimisation model for land use, scale the model from catchment to national levels, quantify regional or national biodiversity, carbon storage, and yield profitability within the UK, and develop the model into a package potentially suitable for commercialisation.

The student will gain a comprehensive understanding of sustainable landscapes within the UK and the necessary subsidy frameworks to achieve optimal conditions. They will review literature to understand the constraints around land parcel allocation and the objectives of different stakeholders, learn how different multi-objective optimisation techniques work, undertake research into formal argumentation, develop dialogical techniques to present reasoning to stakeholders, evaluate the effectiveness of explanations on stakeholder understanding and decision-making, and apply the model to a real-world problem. This project offers a unique opportunity to contribute to a vital area of environmental research and develop a wide range of skills in optimisation, argumentation theory, and stakeholder engagement.

Eligibility: Due to funder restrictions, this project is currently accepting applications from home applicants only.

Application deadline: 12pm on Friday 2 May 2025

This project is currently awaiting final confirmation of industry funding with confirmation expected mid-April. If the CDT has not secured funding, this project may be withdrawn. In this case we will update the status promptly and inform all applicants.

Apply Now

Studentship One

Using AI to Manage Multi-species Grassland for Livestock Farming

Project No: FBS25-73-Parsons-lr

Supervisory Team: Professor Simon Parsons, University of Lincoln; Dr Zoe Barker, University of Reading; Professor Elizabeth Sklar, University of Lincoln

Producing plant matter for animal food is important for livestock farming, both for grazing and for 
making silage. This is typically done by growing fields of rye grass. Switching to a range of species, typically including 
other grasses, legumes like clover, and herbaceous plants like wildflowers, has benefits for the ecosystem and for the animals. The benefits for the ecosystem include improving soil quality, drought tolerance, and biodiversity. The benefits 
for animals include better health and less methane emissions. However, farmers are wary of growing multi-species 
grasslands because they do not understand how best to manage them. This project will gather evidence on management 
practice from ongoing work with multi-species grasslands, and use AI to analyse this evidence and present it farmers to 
help them manage their land.

Training Opportunities

The successful applicant will receive training in AI techniques, the use of the robotic and drone-based technologies which can be deployed in the field to obtain data on grassland, on field work elements like quadrat 
sampling and species identification, and on laboratory analysis. They will also undertake training in the responsible and 
ethical uses of AI.

Project Supervision

An online meeting with the entire supervisory team will be held once a month, and the lead supervisor will meet with the student weekly. These will be one-to-one in the sense that the DTP student will be the only student in those meetings (they will not be lab group meetings). Additional meetings with the wider supervisory group may be scheduled when appropriate (for example for planning field work during the growing season) and the student will participate in lab/group meetings where that is helpful for them. The lead supervisor has a longstanding policy of providing feedback on student paper drafts within 48 hours (in practice the turnaround is typically less). A schedule for feedback on thesis drafts (and sections thereof) varies depending on what exactly is being reviewed, but a schedule is agreed in advance.

Student Profile

This project will be of interest to candidates with either a background in agriculture or agricultural 
technology who are interested in artificial intelligence/machine learning, or to candidates with a background in artificial
intelligence/machine learning who are interested in agriculture. 

Stipend (Salary)

FoodBioSystems DTP students receive an annual tax free stipend (salary) that is paid in instalments throughout the year. 
For 2024/25 this is £19,237 and it will increase slightly each year at rate set by UKRI.

Equity, Diversity, and Inclusion

The FoodBioSystems DTP is committed to equity, diversity, and inclusion (EDI), to building a doctoral researcher (DR) and 
staff body that reflects the diversity of society, and to encourage applications from under-represented and disadvantaged groups. Our actions to promote diversity and inclusion are detailed on the FoodBioSystems DTP website and include:

• Offering reasonable adjustments at interview for shortlisted candidates who have disclosed a disability or 
specific learning difference.
• Guaranteed interview and applicant mentoring schemes for applicants, with UK home fees status, from eligible
under-represented ethnic groups. 

These are opt-in processes. 

Our studentships are offered on a part time basis in addition to full time registration. The minimum registration is 50% FT 
and the studentship end date will be extended to reflect the part-time registration.

For up to date information on funding eligibility, studentship rates and part time registration, please visit the 
FoodBioSystems website.

Applications for this opportunity are by online application form, and full details can be found on the FoodBioSystems website.

Studentship Two

ValueWaste: Revealing the Potential of Organo-Mineral Fertilisers to Enhance Crop Productivity, as Well as Improve Soil Health and Sustainability

Supervisory Team: Dr Leonidas Rempelos, University of Lincoln; Professor Ruben Sakrabani, Cranfield University; Dr Iain Gould, University of Lincoln

The UK imports a significant amount of unsustainably-mined phosphorus, with low deployment efficiency leading to environmental issues like eutrophication. Organic waste streams (e.g., manure, sewage sludge) offer potential for sustainable phosphorus use but are challenging to transport and apply. In addition, issues such as variability  in recovered phosphorus fertiliser nutrient content and stability, low micronutrient absorption in plants, and insufficient assessment of environmental impacts have led to low end-user uptake.

This exciting PhD project aims to develop a groundbreaking solution by using blends of organic waste residues as carriers for the creation of pelleted Organo-Mineral Fertilizers (OMFs) to enhance crop growth, yield, and quality, as well as improve soil health and sustainability, reducing the carbon footprint of crop production. The project will involve the development of a range of pelleted OMFs by blending different combinations/ proportions of organic waste, with varying  levels of mineral macro-micronutrient supplementation and the use of state-of-the-art sensor technologies to compare OMFs nutrient release/availability patterns and assess the potential of multiple wheat genotypes to grow with OMFs. Outputs will be scaled/validated on field trials, where the influence of OMFs accompanied by microbial biostimulants and nutrient-efficient wheat genotypes on enhancing soil health/nutrient dynamics will also be evaluated.

Training Opportunities

The project offers training covering cross-disciplinary technical and transferable skills. Training would be provided via both partners for the use of (1) various multispectral 3D scanners for canopy/root phenotyping, and image analysis to visualize plant growth and calculate a wide variety of morphological/physiological parameters, and (2) AAS, ICP-MS for heavy metal analysis, Elementar analysers for carbon, segmental flow analyser for nitrate, and spectrophotometer for phosphorus analysis. Other training opportunities include the attendance on MSc modules in areas such as soil science, plant genetics, and food quality or image/data processing, data mining, data programming in R/Python. The student will also benefit from the University of Lincoln’s Graduate School training on research skills, scientific writing, and personal development.

Project Supervision

The student will be supported by a lead supervisor and two co-supervisors representing different disciplines. The lead supervisor will be providing overall guidance and feedback, while the other members of the team will be offering specialised support/ additional feedback. In weekly one-to-one supervision and monthly full supervision team meetings progress, challenges, review of experimental results, and plan next steps will be discussed. Written feedback will be provided within two weeks of submission while oral feedback will be given during meetings. To effectively student track progress, clear action items/deadlines will be assigned, ensuring accountability and timely completion of tasks. Decisions made, actions, and follow-up points will be minuted and shared with all participants for reference. The student will also be providing regular progress reports summarizing completed tasks, ongoing work, and challenges faced.

Student Profile

This project is ideal for candidates with at least BSc (2:1) honours degree in Agricultural Sciences and Technology, Agronomy, Plant Science, Soil Science, Environmental and Biological Science, Geography, or Chemistry. Candidates from disciplines which are not directly related to the project research area should demonstrate their ability to learn and apply new concepts and skills. 

Stipend (Salary)

FoodBioSystems DTP students receive an annual tax free stipend (salary) that is paid in instalments throughout the year. For 2024/25 this is £19,237 and it will increase slightly each year at rate set by UKRI.

Equity, Diversity, and Inclusion

The FoodBioSystems DTP is committed to equity, diversity, and inclusion (EDI), to building a doctoral researcher (DR) and staff body that reflects the diversity of society, and to encourage applications from under-represented and disadvantaged groups. Our actions to promote diversity and inclusion are detailed on the FoodBioSystems DTP website and include:

• Offering reasonable adjustments at interview for shortlisted candidates who have disclosed a disability or 
specific learning difference.
• Guaranteed interview and applicant mentoring schemes for applicants, with UK home fees status, from eligible
under-represented ethnic groups. 

These are opt-in processes. 

Our studentships are offered on a part time basis in addition to full time registration. The minimum registration is 50% FT 
and the studentship end date will be extended to reflect the part-time registration.

For up to date information on funding eligibility, studentship rates and part time registration, please visit the 
FoodBioSystems website.

Applications for this opportunity are by online application form, and full details can be found on the FoodBioSystems website.