Research Associate in Machine Learning and Landscape Management (Fixed Term)

The University of Cambridge is one of the world's oldest universities and leading academic centres. If you're looking for a new challenge and would like excellent benefits, extensive learning opportunities and a stimulating working environment in return for your skills and contribution, there could be a job here for you.

Applications are invited for a Research Associate position to work as part of the flagship Natural Environment Research Council (NERC) Centre for Landscape Regeneration (CLR).

https://www.clr.conservation.cam.ac.uk/

The successful candidate will be based in the Department of Computer Science and Technology and will join the research group of Prof Emily Shuckburgh, as well as being part of the Centre for Landscape Regeneration (CLR).

The Centre for Landscape Regeneration is an ambitious programme of research that aims to provide the knowledge and tools needed to regenerate the British countryside using cost-effective nature-based solutions that harness the power of ecosystems to provide broad societal benefits including biodiversity recovery and climate mitigation and adaptation. The focal landscapes for CLR are in the Fens (north of Cambridge), the Cairngorms National Park and the Lake District National Park. The Research Associate will work in partnership with colleagues from multiple departments within the University of Cambridge as well as a range of collaborating organisations.

The role holder will lead the research to develop machine learning based approaches to advance the core objectives of the project. The primary focus will be on identifying optimal land management solutions to delivering food production, nature conservation and greenhouse gas emissions reductions. This will involve deploying machine learning techniques to model a collection of objective functions (e.g. how the abundances of bird species are affected by agricultural yield). A statistical emulation approach will be used to infer optimal solutions and allow a wide range of scenarios to be explored to reveal trade-offs affecting decision-making. Data from remote and in situ sensor technologies will be utilised and the role holder will develop multi-fidelity approaches to synthesise different data sources. Comprehensive climate change risk assessments based on downscaled and bias-corrected climate simulations (also using machine learning) will be conducted for each landscape to assess resilience of landscape restoration solutions to climate change. The role holder could work in all three landscapes or could choose to focus on one.

Eligible candidates must have a PhD in Computer Science or a related discipline (or equivalent experience). A background in machine learning applied in an environmental science domain is essential. Applicants must be highly motivated and should have excellent time management, organisational and communication skills, and be able to work well independently and as part of a team.

The successful candidate will be based in Cambridge. They will have the opportunity to participate in a wide range of departmental and University activities and will be associated with the Institute of Computing for Climate Science, https://iccs.cam.ac.uk/. They will also have the opportunity to participate in a wide range of activities taking place in the David Attenborough Building, which is home to the Cambridge Conservation Initiative partnership of the University of Cambridge and ten international conservation NGOs and networks. https://www.cambridgeconservation.org/

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Fixed-term: The funds for this post are available for 2 years.

Informal queries may be directed to the HR team at personnel-admin@cst.cam.ac.uk.

For further information about the role please contact Helen Driver at hvd23@cam.ac.uk.

Please quote reference NR39523 on your application and in any correspondence about this vacancy.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

The University has a responsibility to ensure that all employees are eligible to live and work in the UK.

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