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Research Fellow in Artificial Intelligence

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About us

The UCL School of Management (SoM) is home to UCL’s business and management research and education programmes. The School has forged a reputation for world-leading research and teaching in the areas of innovation management, the creation and growth of technology-intensive organisations, business analytics, and entrepreneurship. The research undertaken has been rated as ‘world-leading’ and ‘internationally excellent’ in the Research Excellence Framework 2021, placing us 2nd in the UK for Business and Management. The Department of Mathematics has internationally recognised research groups in pure and applied analysis; fluid mechanics; mathematical physics; geometry and topology; algebra, number theory and combinatorics; mathematical modelling in biology, finance, industry and society. There are regular departmental seminar series in financial and applied mathematics, as well as seminar series joint with other London universities in number theory, geometry and analysis. There are regular London-Paris and London-Brussels meetings in analysis, number theory, and geometry.

About the role

UCL SoM and Department of Mathematics are seeking to appoint a Research Fellow in Artificial Intelligence. The post holder will develop innovative AI techniques on bespoke and adaptive methods for automatic Gaussian process emulation of a network of multidisciplinary models, including UK land-use models, climate models, biodiversity models, etc. The fellow will work with economists, mathematicians, statisticians, and research software engineers across the project to develop and implement methods that enable real-time decision support tools for UK’s woodland expansion, a critical step in achieving UK’s commitment to Net Zero by 2050. The fellow can expect to work across disciplines with researchers at UCL and University of Exeter, and engage with our project partners including Defra, National Trust, Network Rail, MoD, Forestry England, National Forestry Company, and Woodland Trust. The post holder will have numerous opportunities to enhance their professional development by attending and presenting at national and international workshops and conferences in both AI and Net Zero communities, expanding their professional network and showcasing their research outcomes. The post is funded by the EPSRC project ADD-TREES (AI-elevated Decision-support via Digital Twins for Restoring and Enhancing Ecosystem Services) and is available immediately until 31 March 2025.

About you

The post holder will hold a PhD in Statistics, Mathematics, Machine Learning, Computer Science, or a closely related field with a substantial quantitative component with sufficient specialist knowledge in statistics, machine learning, numerical modelling, particularly in Gaussian processes and surrogate modelling to develop/follow research programmes and methodologies. The post holder will develop and apply deep Gaussian process emulation and adaptive design for automatic emulation of the non-stationary, spatial-temporal, and non-Gaussian output from different scientific and economic models that are critical to tree-scaping decision problems faced by our users. Key skills include experience of coding in R and experience with fitting statistical and machine learning models, such as Gaussian processes, and an ability to work with a large interdisciplinary team to meet project objectives. It is essential that your application includes: • a cover letter that describes how your qualifications and experience make you a suitable candidate for this position. • a curriculum vitae (including a list of publications). • the names and contact details of two referees. Please ensure you read the Job Description and Person Specification on the application page for full details of this role before your application. For further information please contact Dr Deyu Ming.

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