357 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "U.S" PhD scholarships in United Kingdom
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. Although individually both temperature extremes (e.g. heat-stress) and air quality (e.g. Nitrogen Oxide from car fumes) are known to severely impact human health, it is unclear how these stressors interact
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venues including Serpentine Arts Technologies, Southbank Centre, Barbican Immersive and more, you’ll develop cutting-edge research skills alongside practical expertise in machine learning, extended reality
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, Chemistry, Physics, Engineering, Mathematics, Computer Science, Data Science, Machine Learning or Artificial Intelligence a minimum 2:1 undergraduate degree (or equivalent) Excellent spoken and written
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this industrial PhD studentship in Physics – fully funded by the University of Exeter and Leonardo UK. We’re looking for a student who has a passion for science, with ambition to learn and apply their own ideas
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, analyse learning from past incidents and exercises, and co-develop practical frameworks or tools to support improved cross-agency working. These will be tested through scenario-based evaluation and
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June, 2026 - 12:00 The Open University Business and Law Schools are inviting applications to join our campus-based PhD Programme and our PhD by Distance Learning Programme. The starting date for both
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, or other related academic discipline. Good programming skills (preferably Python). Background/work experience in Cyber Security, Machine Learning, and Finance would be highly beneficial. How to apply
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algorithms that combine Reinforcement Learning techniques like Partially Observable Markov Decision Processes (POMDPs) with cognitive inference modules capable of modelling human beliefs, intentions, and goals
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and kinematic models with machine-learning-based channel state information (CSI) prediction to enable robust, low-latency connectivity across multi-layer NTN systems. This PhD project sits
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(CHF), tailored to complex geometries typical of fusion reactor cooling systems. Compile a comprehensive dataset of boiling parameters to support machine learning-based analysis of two-phase flow