UCL
Job title:
Research Fellow in Statistical Ecology
Company:
UCL
Job description
About usBiosciences 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. The Division has a diverse portfolio addressing all areas of biology from protein interactions to cell function, organism development, genetics, population studies and the environment. Computational modelling approaches are frequently used alongside experimental research programmes and much of our research crosses traditional boundaries, including the relationship of biodiversity to the health of the planet. Activity is underpinned by high calibre science technology platforms and state of the art equipment. Educational activity includes a range of undergraduate programmes, an expanding number of Masters Programmes and a substantial number of postgraduate research students.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 roleYour 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.This appointment is subject to UCL Terms and Conditions of Service for Research and Professional Services Staff. Please visit https://www.ucl.ac.uk/human-resources/conditions-service-research-teaching-and-professional-services-staff for more information.Interviews will take place in early 2024.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, [email protected].
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 youThe 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 offerThe UCL Ways of Working supports colleagues to be successful and happy at UCL through sharing expectations around how we work – please see https://www.ucl.ac.uk/human-resources/policies-advice/ways-working to find out more.As well as the exciting opportunities this role presents, we also offer some great benefits some of which are below:
- 41 Days holiday (27 days annual leave 8 bank holiday and 6 closure days)
- Additional 5 days’ annual leave purchase scheme
- Defined benefit career average revalued earnings pension scheme (CARE)
- Cycle to work scheme and season ticket loan
- Immigration loan and expenses
- Relocation scheme for certain posts
- On-Site nursery
- On-site gym
- Enhanced maternity, paternity and adoption pay
- Employee assistance programme: Staff Support Service
- Discounted medical insurance
Visit https://www.ucl.ac.uk/work-at-ucl/reward-and-benefits to find out more.Our commitment to Equality, Diversity and InclusionAs London’s Global University, we know diversity fosters creativity and innovation, and we want our community to represent the diversity of the world’s talent. We are committed to equality of opportunity, to being fair and inclusive, and to being a place where we all belong.We therefore particularly encourage applications from candidates who are likely to be underrepresented in UCL’s workforce.These include people from Black, Asian and ethnic minority backgrounds; disabled people; LGBTQI+ people; and for our Grade 9 and 10 roles, women.Our division holds an Athena SWAN Silver award, in recognition of our commitment to advancing gender equality.You can read more about our commitment to Equality, Diversity and Inclusion here: https://www.ucl.ac.uk/equality-diversity-inclusion/
Expected salary
Location
London
Job date
Sat, 21 Dec 2024 23:27:49 GMT
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