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to the development of robust, reproducible machine learning workflows. The role forms part of a nationally significant research programme with strong policy relevance, working in collaboration with the Imago Data
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crystallography aspects of our research (predominantly in the area of crystalline materials), while also ensuring integration with the other research areas in the Cooper Group (i.e., robotic automation and machine
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of machine learning models, and conformal inference. Applicants should demonstrate scientific creativity, research independence, the capacity to support junior team members, and strong communication skills
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that integrate multi-omics data to uncover mechanisms of disease, cellular resilience, and therapeutic response. The post holder will lead research applying large-scale machine learning and foundation models
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contribute to the development of robust, reproducible machine learning workflows. The role forms part of a nationally significant research programme with strong policy relevance, working in collaboration with
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for stress-testing. Training spans multi-agent reinforcement learning, evolutionary computation, adversarial machine learning, game-theoretic modeling, and financial crime compliance. You will design agent
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. The research will integrate advanced full-field imaging techniques, including X-ray computed tomography, neutron tomography, and related methods, with modern machine-learning approaches such as sparse regression
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machine learning (ML) and artificial intelligence (AI) workflows, the project aims to create a comprehensive molecular atlas and identify novel, translational biomarkers and therapeutic targets. Project
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: Earth Sciences, Bioscience, Interdisciplinary Life and Environmental Science, Inorganic Materials for Advanced Manufacturing, Chemical Synthesis for a Healthy Planet,Statistics and Statistical Machine
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fundamental research, we create widely used open-source software including autodE, cgbind/C3, and mlp-train. Our recent advances in Machine Learning Interatomic Potentials (MLIPs) form the foundation of our ERC