250 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"UCL" PhD positions in United Kingdom
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characterization, computational modelling, or metal forming is advantageous but not essential. Enthusiasm and willingness to learn are more important. You will gain hands-on experience with cutting-edge experimental
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, reinforcement learning for environmental control, explainable AI for autonomous decision-making, multi-objective optimisation, and large language models as human–building interfaces. Robotics and Actuation
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calibration of standard petrophysical apparatus and bespoke high-pressure, high-temperature deformation machines. • Geochemical and fluid analysis techniques You will join a diverse and collaborative research
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: Applicants should have, or expect to achieve, at least a 2:1 honours degree (or equivalent) in a relevant subject, typically in the physical sciences or engineering. Experience of computer simulation, or
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control strategies integrating fuel, engine, electric machine, and energy recovery systems for improved overall efficiency. Validate the developed methods through experimental and simulation studies
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students per year across a number of UK institutions. The Skills and Training Hub will run online and face-to-face activities to facilitate cohort building and group learning exercises throughout the PhD
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aircraft, utilized for research into thermal management and system health monitoring, supporting studies in military aircraft systems. Engaging with these facilities allows students to acquire practical
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students Subject areas Computer sciences Physical & Environmental Sciences Project description The accelerating impacts of climate change—particularly those related to water, such as flooding, coastal
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validation with end-users. The student will have access to specialised training in quantum security and advanced machine learning. The self-funded nature of the project affords the unique flexibility to pursue
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Applications are invited for an employed Doctoral Candidate to be funded by the Marie-Skłodowska-Curie Learning Network for Decentralized critical Infrastructure Asset Monitoring and coNDition