About the role
Your role will be to help design and develop a new suite of computational and statistical tools for forecasting the stability of ecological communities under environmental change. This will involve combining tools from across parametric statistics, machine learning, dynamical systems, and deep learning to build and benchmark novel computational approaches against large, real-world datasets. The initial focus will be on Bayesian hierarchical modelling, but the role will demand moving fluidly between methodological frameworks and tools to overcome computational challenges as they arise. The primary application will centre on using environmental, functional, and phylogenetic data to predict forest community composition, with existing plant, aquatic, and microbial datasets used for model testing and development.
This is a fixed-term role for 24 months, with the possibility to extend. Start date is negotiable, but ideally as soon as possible (Q3 of ).
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 (salary £34, - £36, per annum) with payment at Grade 7 being backdated to the date of final submission of the PhD Thesis. This appointment is subject to UCL Terms and Conditions of Service for Research and Professional Services Staff. Please visit for more information.
Interviews will take place in the early summer, .
A job description and person specification can be accessed at the bottom of this page.
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 Biosciences staffing at .
About you
The successful candidate will hold or be completing a PhD in a quantitative discipline such as statistics, mathematics, computer science, computational ecology, or a related field. Strong training in mathematics, statistics, and computational tools is essential, enabling you to implement and adapt methods ranging from parametric statistical inference (including Bayesian approaches) to deep learning. Expertise in any single area is not required, but you should be comfortable working across domains and moving quickly between computational and mathematical approaches as problems demand. Your research (papers or dissertation) must demonstrate novel application of statistical, mathematical, machine-learning, or computational methods. Some experience conducting computational research on large, real-world datasets is required, and experience with biological or environmental data is helpful. Expertise in scientific computing and advanced programming is essential (ideally R, Python, C, or Julia), along with extensive experience using scientific computing infrastructure (, Linux/Unix, parallel computing, HPC, GPU) and collaborative research practices (version control, scripted and reproducible workflows). Knowledge of community ecology, forest ecology, or theoretical ecology is likewise useful but not required. Excellent written and verbal communication skills are essential.
What we offer
As well as the exciting opportunities this role presents, we also offer some great benefits some of which are below:
1. 41 Days holiday (27 days annual leave 8 bank holiday and 6 closure days)
2. Additional 5 days’ annual leave purchase scheme
3. Defined benefit career average revalued earnings pension scheme (CARE)
4. Cycle to work scheme and season ticket loan