Fixed-term

The student will be part of a multidisciplinary effort to investigate the anatomical, physical and cellular factors that shape internal root environments. The project will explore how root organisation and environmental conditions combine to influence oxygen availability, and how these internal conditions vary across space and time. Depending on the student’s interests and skills, the project may involve a combination of: Experimental studies using model plant species to examine root oxygen status under contrasting environmental conditions. Application of imaging- and sensor-based approaches to visualise and quantify oxygen dynamics in roots and their surrounding environment. Analysis of how root tissue organisation and cellular connectivity influence internal microenvironments. Integration of experimental observations with quantitative or computational frameworks, developed in collaboration with partners with modelling expertise. This PhD offers the opportunity to work at the interface of plant physiology, root biology, imaging and quantitative analysis. The student’s work will provide key conceptual and experimental foundations that support other strands of the Breathing Underground project, while allowing scope to develop independent questions within the broader theme. Award Start Date: 01/10/2026 Duration of Award: 48 months This research studentship is only available to UK citizens and includes payment of tuition fees and a tax-free stipend based on current BBSRC rates. Applicant Qualification Requirements Applicants should be highly motivated, curious and keen to develop expertise at the interface of plant biology and quantitative analysis. Candidates should a Master’s degree, in Plant Science, Biology or a closely related discipline. Experience or interest in one or more of the following areas would be advantageous (full training will be provided as required): • Plant physiology, root biology or plant–environment interactions. • Experimental approaches to studying internal plant environments or spatially structured biological processes. • Imaging, sensor-based measurements or quantitative data analysis. • Basic computational or programming skills, or a willingness to engage with modelling and data-driven approaches. Strong communication skills, enthusiasm for interdisciplinary research, and a commitment to rigorous and reproducible science are essential. Previous applicants need not apply. How to Apply Prospective applicants are strongly encouraged to get in touch to discuss the project and their suitability. Informal enquiries may be addressed to vinay.shukla@nottingham.ac.uk. Applications should be submitted by emailing a detailed CV and cover letter to vinay.shukla@nottingham.ac.uk by the stated closing date. A complete application should include: A detailed CV, clearly outlining academic background, research experience and technical skills. Applicants are encouraged to highlight: o Relevant coursework, research projects, or thesis work. o Experience with experimental plant biology, physiology, imaging, sensors, or quantitative analysis (where applicable). o Any computational, data analysis or programming experience, including software or languages used. o Publications, preprints, conference presentations or posters (if available). o Names and contact details of two academic referees. A cover letter (typically 1–2 pages) explaining: o Motivation for applying to this PhD project. o Relevant experience and skills, and how these align with the project. o Research interests and career aspirations. o Why you are interested in working within the Breathing Underground programme and at the University of Nottingham.

Details Biodiversity monitoring is the cornerstone of understanding responses to disturbance, ecological resilience, the effect of interventions in degraded ecosystems and progress towards conservation targets. Considerable biodiversity data are collected from large-scale monitoring efforts as well as from opportunistic observations and are collated into databases and repositories to support our understanding of the state of biodiversity. As a result, it has become clear that much of our planet’s biodiversity is in decline as a direct result of human pressures (WWF 2024, Burns et al 2023, IPBES 2019) However, much of the biodiversity data we collect is primarily focused on charismatic animals, in particular vertebrates (Troudet et al 2017), giving us a limited picture of ecosystem health and function from lower trophic levels. The challenge of using observation-based data also manifests in limitations in spatial and temporal scales and coverage. This studentship will investigate environmental DNA as a tool to complement and supplement biodiversity datasets to facilitate a wider understanding of ecosystem health and function. Dr Littlefair’s lab and collaborators recently explored the possibility of biodiversity monitoring through airborne environmental DNA by air quality networks operating at weekly temporal intervals (Littlefair et al 2023). By combining eDNA with autonomous monitoring systems, as well as investigating the wealth of existing open source eDNA data, we will increase the coverage of the spatio-temporal-taxonomic continuum of existing types of biodiversity data (Tournayre et al 2025a,b). This studentship will achieve this through joint modelling approaches of observation and DNA-based data at varying temporal and spatial scales. Depending on the student’s interest, it will be possible to investigate 1) coverage and uncertainties inherent in each data type (Diana et al, 2025), 2) the response to ecological perturbation evaluated by each data type (Ji et al, 2025), 3) the role of eDNA in assessing progress towards biodiversity targets. This project will support the student to gain cutting-edge data handling and statistical modelling skills, as well as an in-depth knowledge of a diversity of data types including citizen science observations / structured surveys / and high throughput environmental genetics. The student will gain knowledge of broad scale biodiversity trends which will serve them in further research-focused positions, or in industry, governmental or NGO work. The student will join a dynamic and growing lab which is supported by several funding sources including a Future Leaders Fellowship. Dr Littlefair’s lab is based at the newly opened People and Nature group at UCL East (Queen Elizabeth Olympic Park campus, Stratford). The student will also benefit from the academic network of co-supervisors Dr Charlie Outhwaite (ZSL) and Dr Eleni Matechou (School of Mathematical Sciences, Queen Mary University of London), as well as the rich intellectual environment of London (CBER, CEE, NHM, Kew). We are open to hearing from students from interdisciplinary backgrounds in ecology, environmental science, quantitative/computational ecology, data science, AI, or mathematics. If from an ecological background, students must have demonstrable experience or interest in big datasets/statistical modelling. If from a data science or mathematical background, students must have demonstrable experience or interest in answering ecological questions. The application process is in two stages which both need to be completed. You will need to 1) Complete the UCL GEE application process here, selecting admission in October 2026, and 2) Email the following documents to Dr Joanne Littlefair j.littlefair@ucl.ac.uk with the subject heading “Environmental DNA for biodiversity data PhD studentship”: 1) Your CV 2) A motivation statement which includes: a) a brief description of up to two research projects that you’ve undertaken, written for a non-specialist (but still scientific) audience (1 paragraph per project) b) a summary of your pathway to this opportunity, including any challenges that you’ve had to overcome in your research or educational career so far if you’d like to share them, and an explanation of why this opportunity appeals to you (max 3 paragraphs) c) Confirmation that you meet the requirements for Home fee status, set out here: https://www.ucl.ac.uk/students/fees-and-funding/pay-your-fees/fee-schedules/student-fee-status (fee status is formally assessed by UCL after application). Funding Notes This is a directly funded PhD covering home (UK) fees and an annual stipend for three years at the UKRI standard rate with London allowance. References Cooke, Rob, Charlotte L. Outhwaite, Andrew J. Bladon, Joseph Millard, James G. Rodger, Zhaoke Dong, Ellie E. Dyer, et al. ‘Integrating Multiple Evidence Streams to Understand Insect Biodiversity Change’. Science, 4 April 2025. https://www.science.org/doi/10.1126/science.adq2110. Diana, A., Matechou, E., Griffin, J., Yu, D. W., Luo, M., Tosa, M., … & Griffiths, R. A. (2025). eDNAplus: A unifying modeling framework for DNA-based biodiversity monitoring. https://www.tandfonline.com/doi/full/10.1080/01621459.2024.2412362 Ji, Y., Diana, A., Li, X., Matechou, E., Griffin, J. E., Liu, S., … & Popescu, V. D. (2025). High Quality, Granular, Timely, Trustworthy and Efficient Vertebrate Species Distribution Data Across a 30,000 km2 Protected Area Complex. https://onlinelibrary.wiley.com/doi/abs/10.1111/ele.70302 Littlefair et al 2023 Air-quality networks collect environmental DNA with the potential to measure biodiversity at continental scales https://doi.org/10.1016/j.cub.2023.04.036 Outhwaite, C.L., McCann, P. & Newbold, T. Agriculture and climate change are reshaping insect biodiversity worldwide. Nature 605, 97–102 (2022). https://doi.org/10.1038/s41586-022-04644-x Outhwaite, C. L., Gregory, R. D., Chandler, R. E., Collen, B., & Isaac, N. J. B. (2020). Complex long-term biodiversity change among invertebrates, bryophytes and lichens. Nature Ecology and Evolution, 1–9. doi: 10.1038/s41559-020-1111-z Tournayre et al 2025a First national survey of terrestrial biodiversity using airborne eDNA https://doi.org/10.1038/s41598-025-03650-z Tournayre et al 2025b Contrasted effects of human pressure on biodiversity in the UK: a multi-taxonomic assessment using airborne environmental DNA https://doi.org/10.1002/ecog.08196 Troudet et al. 2017 Taxonomic bias in biodiversity data and societal preferences https://doi.org/10.1038/s41598-017-09084-6 Apply Now