Research Associate in Machine Learning (Fixed Term)

A position exists for a Research Associate (RA) in the Department of Engineering to work on Project Cumulus, led by Professor Richard Turner.

The post holder will be located in Central Cambridge, Cambridgeshire, UK.

The Research Associate will join Project Cumulus a collaborative initiative developing AI-powered weather forecasting systems tailored for farming communities in Africa, particularly in Ghana and Senegal.

The Research Associate will contribute to the development of global AI weather models for sub-seasonal forecasting in Africa. They will design and implement modular AI forecasting systems, including data encoders, processors, and task-specific decoders, integrating remote sensing and numerical weather prediction inputs to generate agriculturally relevant predictions. In addition, they will support the development of AI-based downscaling and post-processing methods, including end-to-end integration and fine-tuning. The Research Associate will evaluate the new AI methods on sub seasonal benchmarks and publish the findings.

The Research Associate will work closely with UK and African partners facilitating in-country testing and operationalization of the new AI approaches.

This role will be part of the End-to-end forecasting team, and you will be reporting to Professor Richard E. Turner and working closely with Senior Research Associates and Research Associates within the same area.

The skills, qualifications and experience required to perform the role of Research Associate are:

• Holds or is about to be awarded a PhD in a relevant specialist subject: Computer Science, Information Engineering, Statistics or a related field of study.
• Essential: Experience of deep learning, software engineering, and strong mathematical skills.
• Desirable: Experience in weather, environmental forecasting, spatio-temporal modelling, or related fields.

The successful candidate may be required to undertake travel as necessary to support the duties and responsibilities of the role.

Teaching may be required as part of collaborative work undertaken within the role.

Fixed-term: The funds for this post are available until 31 March 2028 in the first instance.

To apply online for this vacancy and to view further information about the role, please click 'Apply' above.

Please ensure that you upload your Curriculum Vitae (Resume) including a list of your publications and a covering letter in the 'Upload' section of the online application. If you upload any additional documents which have not been requested, we will not be able to consider these as part of your application.

If you have any questions about this vacancy or the application process, please email Kimberly Cole, Divisional Administrator, Division F (Information Engineering):

Please quote reference NM50807 on your application and in any correspondence about this vacancy.

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