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

The funds for this post are available until 31 January 2027 in the first instance.

Applications are invited for a Research Assistant/Associate position to work as part of the flagship Centre for Landscape Regeneration (CLR) led by the University of Cambridge

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 (CLR) 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 the CLR are the Cambridgeshire Fens, the Cairngorms and the Cumbrian Lake District. The Research Assistant/ Associate will work in partnership with colleagues from multiple departments within the University of Cambridge as well as the collaborating organisations (RSBP, NIAB and UKCEH).

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 or two.

Eligible candidates must have (or be close to submitting) 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 collaborative, interdisciplinary team.

Appointment at Research Associate level is dependent on having a PhD. Those who have submitted but not yet received their PhD will be appointed at Research Assistant level, which will be amended to Research Associate once the PhD has been awarded.

The successful candidate will be based in Cambridge and regular office attendance will be required. They will have the opportunity to participate in a wide range of departmental and University activities and will also be associated with the Institute of Computing for Climate Science, . 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 (CCI) partnership of the University of Cambridge and ten international conservation NGOs and networks.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society. We also support family-friendliness, and we welcome applications from individuals who wish to be considered for part time or flexible working arrangements.