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to players’ needs in real time. The successful candidate will work with an interdisciplinary supervisory team, benefiting from expertise in adaptive systems, accessibility and human-computer interaction, and
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monitoring. Candidates should have a background in computer science, AI, machine learning, affective computing, computational psychology or related areas. Strong programming skills are essential. Funding
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discipline. Prior experience in any of the following is a plus but not essential: ultrasound or wave physics, numerical simulation, Python programming, and machine learning frameworks. Most importantly, we
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the impacts of extreme heat exposure on learning and decision-making, as relevant to mental health. This is a full-time role, based in Central Cambridge. The primary function of this post is to undertake
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, biomedical engineering, advanced image processing and machine learning. The studentship suits a candidate with a strong background in optometry, physics, engineering, computer science or a related discipline
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Announcing 10 PhD Studentships within the Royal Holloway Social Purpose Centre for Doctoral Training
activities (including “The Other Kind of Doctor” podcast and blog), annual conference, and opportunities to connect and engage with PGRs outside your main discipline. Details of the award: The studentship
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. This project proposes the development of a new CFD simulator for offshore renewable energy applications based on physics-informed deep learning that offers greater efficiency and robustness. This is a unique and
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Campylobacter disease burden is assessed, identify drivers of change and possible interventions. You will explore how genomic diversity relates to clinical outcomes, whether machine‑learning approaches can
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heterogeneity govern transport dynamics and degradation mechanisms during extended operation. A coupled mechanical–transport framework, accelerated through machine-learning surrogate models trained on multiscale
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simulation results with experimental data. This project will integrate advanced AI techniques, including machine learning for parameter optimisation (e.g., Bayesian optimisation, reinforcement learning), AI