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on corporate misconduct, including financial fraud, insider trading, and bribery, utilizing advanced machine learning techniques. The position will be based at the ULMS campus with the option for hybrid working
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expertise in large-scale data analysis e.g. using HPC. You should have a PhD in structural bioinformatics, or related area, and interest in understanding protein structure/function. Knowledge of machine
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, artificial intelligence/machine learning, digital twins, and blockchain technology for operations and maintenance. This position is part of the RESTORE project, which aims to develop, test, and deploy
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of materials (e.g., combining structure prediction and machine learning). For example, we work closely with experts in computer science as part of the Leverhulme Research Centre for Functional Materials Design
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accelerating wound-healing. This role focuses on machine-learning and high-throughput methodologies to accelerate the experimental design and optimisation of these microgels. The project is part of a
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techniques and machine learning) and/or orbit determination. A proven track record in trajectory design and optimisation are highly required. Track record of experimental (e.g. air-bearing spacecraft simulator
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machine learning is essential; while structure prediction or materials chemistry experience would be advantageous, it is not a pre-requisite for the role. This post would be ideal for an ambitious and
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Navigation of Chemical Space for Function¿. This team is developing a new approach to materials design and discovery that combines experiment with computation, exploiting both machine learning and symbolic AI