Research Fellow in Statistical Ecology

About us

Biosciences is one of the world’s foremost centres for research and teaching in the biological sciences and one of the largest Divisions within UCL, undertaking a significant amount of research and teaching.

This is an exciting opportunity to join an interdisciplinary team of researchers at UCL to develop new statistical approaches to forecast how forest ecosystems will respond to environmental change. This project is the first phase of a 5-year ERC Starting Grant, supervised by Dr Maynard, which aims to develop a novel framework to forecast survival and responsiveness of communities under disturbance. The research fellow will primarily be based at the People and Nature Lab at the new UCL East campus—a collaborative group of researchers addressing the intersection between biodiversity, technology, computer science, the built environment, and society to create new ways for societies and nature to sustainably coexist.

About the role

Your role will be to develop and apply novel computational and statistical tools to predict the stability of ecological communities. This will involve constructing, optimising, and testing Bayesian hierarchical models and machine learning models, with the aim of predicting community composition and coexistence using environmental-, functional-, and phylogenetic information. Applications and model-testing will focus on existing datasets, primarily of forest ecosystems, but will also include plant, aquatic, and microbial communities as needed.

This role is an open-ended role with funding for up to three years. Start date is negotiable, but ideally in Q1 of 2025.

Appointment at Grade 7 is dependent upon having been awarded a PhD; if this is not the case, initial appointment will be at Grade 6B with payment at Grade 7 being backdated to the date of final submission of the PhD Thesis.

Interviews will take place in early 2024.

Job Ref: B02-08137

Closing Date: 29/01/2025 – 23:59 (UK time)

A job description and person specification can be accessed via the “apply” button.

If you have any queries about the role, please contact Dr Daniel Maynard. If you need reasonable adjustments or a more accessible format to apply for this job online or have any queries about the application process, please contact the HR Administrator.

About you

The successful candidate must hold or be submitting a PhD in a relevant area, including an ecological or environmental discipline, statistics, computer science, data science, or related field. You must have strong computational skills and significant experience working on complex statistical models, ideally using Bayesian or machine-learning approaches. Strong programming knowledge in at least one language are required, ideally in R, Python, or Julia.

Experience using Git, the Stan language, parallel and distributed computing, and/or shell scripting are strongly encouraged but not required. Some knowledge of plant ecology, forest ecology, and/or theoretical ecology is useful but likewise not required.

Experience with the peer-review process is mandatory, as is the ability to prepare initial and final drafts of manuscripts for publication. Excellent written and verbal communication skills are essential, including the ability to keep meticulous records and well-annotated computer code.

What we offer

As well as the exciting opportunities this role presents, we also offer some great benefits. Visit https://www.ucl.ac.uk/work-at-ucl/reward-and-benefits to find out more.

Our commitment to Equality, Diversity and Inclusion

You can read more about our commitment to Equality, Diversity and Inclusion here: https://www.ucl.ac.uk/equality-diversity-inclusion/

Customer advert reference: B02-08137

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