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Field
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medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and astrostatistics. These posts
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engineering science, with knowledge and/or some experience of energy technology and policy; and/or quantitative analysis including econometrics, statistics and machine learning and related disciplines handling
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with machine learning techniques for robotic decision-making and intelligent control for tasks with high uncertainties. Experience with research on multi-agent collaboration and decentralized control
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use of molecular assays such as real-time PCR and next-generation sequencing to study the prevalence of infectious diseases in the target study populations. Learning about sample testing under a
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collaboration with industry partners. This work will apply optimal control theory, including machine-learning algorithms and Bayesian estimation, to coherent control of nitrogen-vacancy centers in diamond
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conditions. The researcher will also work with team members within the consortium in generating necessary data required for developing a machine learning model for storm surge prediction. Key Responsibilities
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a track record in computational modelling that explores the dynamics of AI systems and the development of autonomous AI agents, experience with machine learning, reinforcement learning, and generative
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conditions. The researcher will also work with team members within the consortium in generating necessary data required for developing a machine learning model for storm surge prediction. Key Responsibilities
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. The researcher would be expected to have knowledge of protein structure, protein ligand binding, machine learning and expertise in workflow development. Information generated by the project will be widely
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candidate in the area of machine learning for IoT networks. The candidate must hold (or about to complete) a PhD in the related fields shown below. The candidate is expected to have hands-on experience in