Fixed‑term: the funding for this post is available for 12 months in the first instance.
The successful candidate will be based in the Department of Computer Science and Technology and will join Dr Emily Shuckburgh’s research group, 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 CLR are the East Anglian Fens, the Cairngorms and the Cumbrian Lake District.
Responsibilities
The role holder will lead the research to develop a specific machine‑learning based approach to advance the core objectives of the project. The primary focus will be on identifying optimal land‑management solutions to balance food production, greenhouse‑gas emissions reduction, nature conservation and economic co‑benefits. This will involve deploying machine learning techniques to model a collection of objective functions, covering land‑based greenhouse‑gas emissions, agricultural yield, markers of biodiversity and economic benefits. An encoder‑decoder framework will be used to reduce the dimensionality of spatially distributed models to enable the use of multi‑objective optimisation frameworks for generating optimal solutions and allow a wide range of scenarios to be explored and reveal trade‑offs affecting decision‑making. A secondary goal will be developing a methodology for the encoding of spatial constraints into the latent space to ensure only physically viable solutions are generated. The role holder will develop their approaches within one of the CLR landscapes with a view to applying it across all three. The role holder will be expected to engage with relevant stakeholders to ensure their needs are incorporated into the research design. The ultimate ambition is to create interpretable frameworks for decision makers, including decision‑support tools based on the research undertaken in the project and the role holder will be expected to lay the foundations for this.
Qualifications
The ideal candidate should hold a PhD in a relevant specialist subject or is about to submit, or should have the equivalent experience. A background in machine learning applied in an environmental science domain is essential, with a track record of research publications commensurate with the stage of academic career.
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 Department of Computer Science and Technology is an academic department that encompasses computer science along with many aspects of engineering, technology and mathematics. It has a worldwide reputation for academic research with consistent top research ratings. The Department has an open and collaborative culture, supporting revolutionary fundamental computer science research, strong cross‑cutting collaborations internally and externally, and ideas which transform computing outside the University. Please visit https://www.cst.cam.ac.uk to find out more about our department.
Benefits
In addition to the base salary stated above, the successful candidate for this post will receive an additional 2.5% supplement to their pay.
How to Apply
Please provide a CV and covering letter. If you upload any additional documents which haven’t been requested, we will not be able to consider these as part of your application. Please quote reference NR49243 on your application and in any correspondence about this vacancy.
Equal Opportunity
The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.
Visa Support
The University has a responsibility to ensure that all employees are eligible to live and work in the UK. If a visa is required, the University provides support for the application and reimburses the cost of the first visa.
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