Research Associate Machine Learning and Data

2 weeks ago


Loughborough, United Kingdom Loughborough University Full time

**2x Research Associates in Machine Learning and Data Science for Environmental Science**

Department of Computer Science, School of Science

We are looking for two outstanding Research Associates with experience in machine learning and data science to work on the multi-disciplinary £2.5 million EPSRC grant “Self-Learning Digital Twins for Sustainable Land”. The project aims to develop innovative solutions, enhanced by machine learning and digital twin, to improve our knowledge and decision-making for sustainable agriculture and GHG emission reduction from livestock and land use.

Research Associate 1: start ASAP, full-time, contract until March, 2025.

Research Associate 2: start ASAP, full-time, fixed-term contract for 12 months.

You will work with leading researchers from three research-intensive universities - Loughborough, Leicester and Bristol, with complementary expertise in AI, environmental research, digital twin, GHG modelling, behavioural and social science. The Research Associates will explore a wide range of real-world data (e.g. livestock farM/Field measurement and/or image data) and develop novel methods and predictive models to infer new hypotheses and support decision-making in net zero scenarios for sustainable agricultural livestock management. The research aims to close a knowledge gap of how the UK can eliminate agriculture GHG emissions and facilitate the digital transformation of environment-friendly agriculture to achieve net zero carbon ambition.

Applicants should hold a relevant PhD/MSc (or be near completion) degree with experience in machine learning, data science, or computer vision. More details about this post can be found in the attached job description.

Informal inquiries are welcome and should be directed to Prof. Baihua Li,

**Visa Sponsorship**:
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- Qualify as a “new entrant”. For more information on this please see paragraphs SW 12.1 - SW 12.7 of the Immigration Rules Appendix Skilled Worker.

Please follow this link for further details



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