UCL Department of Mathematics and School of Management seek to appoint a post-doctoral Research Fellow in Statistical Science as a part of the EPSRC-funded project ‘ADD-TREES’ (AI-elevated Decision-support via Digital Twins for Restoring and Enhancing Ecosystem Services). ‘ADD-TREES’ will deliver co-designed decision support tools for tree planting and for policies to encourage tree planting to our project partners in Defra, The National Trust, Network Rail, etc. The project is made up of world leading multi-disciplinary researchers focussed on AI (Artificial Intelligence) and Net Zero challenges from UCL and University of Exeter including Department of Mathematics and Statistics, the Department of Geography, and the Land, Environment and Economics Policy (LEEP) Institute. Explore more about the project at: https://netzeroplus.ac.uk/add-trees-project/.
About the role
The post will focus on developing innovative statistical techniques for automatic emulation (i.e., surrogate modelling) of state of the art and computationally expensive computer models for tree growth, soil carbon, agricultural yields, farm profits, biodiversity change, flood risk and pest/disease risk. The successful applicant will help develop the necessary techniques for their emulators to be linked, forming digital twins that enable decision makers to see the potential impact of their decisions on greenhouse gas emissions reduction, biodiversity, food production and other key ecosystem services in real-time and enables them to learn from relevant data as it is collected. The post holder will work with our team of statisticians, economists, environmental modellers, mathematicians, and research software engineers (RSEs) across the project to develop and implement methods that ensure the constructed emulators are effective, robust, and can be run in real-time for embedding in custom decision support tools. The post holder will be supported to publish in leading journals, to speak at international conferences, and to develop as a highly skilled interdisciplinary research team member. The post is fully funded at the salary Grade 7 (£40,524 - £48,763) and available immediately until 31 March 2025. For further information about this role, please check the Job Description (JD) attached below.
About you
The successful applicant will have a PhD in Statistics, Mathematics, Machine Learning, Computer Science, or a closely related field with a substantial quantitative component. Experience of coding in R and fitting statistical and/or machine learning models is essential. An ability to develop the skills to present research in papers and to communicate effectively in project meetings and at conferences is also essential. Experience with computer models, environmental modelling, uncertainty quantification, and other programming languages such as Python and C++ are all desirable. Your application should address the criteria outlined in the Person Specification of the JD and include: 1) a cover letter (max 2 pages) that describes how your qualifications and experience make you a suitable candidate for this position, 2) a curriculum vitae (including a list of publications), and 3) the names and contact details of two referees.
What we offer
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 • Enhanced maternity, paternity and adoption pay • Employee assistance programme: Staff Support Service • Discounted medical insurance
Our commitment to Equality, Diversity and Inclusion
As 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.
To view the job description and apply, please see here.