261 machine-learning-"https:"-"https:"-"https:"-"https:"-"UCL" PhD positions in United Kingdom
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synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application to the analysis of time series. In particular, the project
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/10.1021/acs.jpcb.4c01558 ], but they lack accuracy for predictive modelling. Transferable machine learning potentials, like MACE-OFF [https://doi.org/10.1021/jacs.4c07099 ], effectively achieve quantum
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programming skills. Expertise in developing computer vision and machine learning algorithms would be desirable, highly motivated and enthusiastic about advancing AI for societal impact. Qualifications A high
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Requirements We are seeking enthusiastic, curious, and motivated individuals with: A strong academic background in computer science, artificial intelligence, machine learning, data science, engineering, or a
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Although large numbers of adults attend Welsh for Adults classes nationally, statistics from 2019–2024 show that only around 26% progress beyond Entry level (CEFR A1; National Centre for Learning
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these barriers by putting together a world-leading data resource on suicide and self-harm, and powerful machine learning methodologies compatible with epidemiological principles to produce high-quality evidence
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Learning heat: Physics-Informed Fourier Neural Operators for High-Fidelity Thermal NDE Modern non-destructive evaluation (NDE) increasingly relies on AI models that can reason with physics, scale
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) Start date: September 2026 Project Title: HEALS: Heat, Health, and Learning: Co-Designing Climate-Resilient Strategies for Primary Schools in Oxfordshire Director of Studies: Prof Rajat Gupta Supervisors
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filled The overarching aim of this project is to find synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application
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-agent reinforcement learning (MARL) framework for cyber-physical networked fault-tolerant control of renewable energy-fed smart grids under adversarial conditions [6]-[9]. Multiple autonomous agents will