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developing cutting-edge active-learning (Bayesian optimisation) methods that integrate chemical knowledge by capitalising on Large Language Models (LLMs) as well as human knowledge. You should have a PhD in
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their surfaces. Machine learning methods are used to close the complexity gap. Currently, the group consists of three full professors, one associate professor, 6 postdocs and about 15 PhD and 7 master
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operational practices • Systematically exploring different formulations of mixed-integer constraints in grid optimisation problems • Developing machine learning models to accelerate mixed-integer
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, and machine learning. The environment at GBI will allow researchers to undertake ambitious, long-term, collaborative research, and we will actively support the translation of research to commercial
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functional genomics, bioinformatics, and machine learning to support the generation, interpretation, and screening of large-scale experimental and computational outputs. The successful candidate will
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science, engineering, or a related discipline, with significant postdoctoral research experience. The ideal candidate will have strong expertise in computational biology, machine learning, and quantitative analysis
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machine-learning-based approaches, and to evaluate the thermodynamic costs of quantum operations. You should work effectively as part of a team and engage constructively with collaborators. You will hold a
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(Research Assistant) or PhD degree (Research Associate) in computer science or a related area or equivalent experience. Familiarity with standard machine learning libraries/data analysis, specifically as
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imaging datasets and advanced machine learning approaches to identify novel imaging markers of mental health disorders and cognitive function; 2) developing robust MRI-based acquisition, image
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in theory of probability and statistics, machine learning, or formal methods. The post is available from 2 March 2026 until 1 March 2028. If you are still awaiting your PhD to be awarded you will be