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Field
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laser experimentation to study and control complex nonlinear dynamics. Key Responsibilities: Develop and implement Physics-Informed Neural Network (PINN) models to simulate, predict, and analyze complex
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. This role is ideally suited to a researcher with experience in computational structural biology, glycobiology, or protein modelling, who is interested in applying AI-enabled structural prediction and glycan
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of pharmaceutical formulation and manufacturing processes. The role The post holder will develop and implement mechanistic models to analyse and predict the behaviour of pharmaceutical processes. Your work will
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models and simulations that deepen the understanding of the underlying physics involved. The ultimate goal is to create predictive, physics-based models that optimise and control pharmaceutical
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models and predictive tools to improve battery performance, lifetime and safety. RaRR is a new project commenced in WMG in October 2025, funded by EPSRC and HVMC, with a focus on second-life battery
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contribute to the development of next-generation modelling frameworks that combine physics-based hydrodynamic modelling with artificial intelligence (AI) and data-driven methods to better predict contaminant
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based on Machine Learning (ML) emulators have taken the weather predictions research by storm, as they run faster and use less energy than traditional approaches: numerical models based on physical
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an advantage. The appointee will work with Prof. Wai Sze Chan on projects related to developing predictive models for health outcomes and digital behavioural interventions. Responsibilities include managing and
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other life-threatening illnesses. Our dedicated and compassionate faculty and staff are driven by a common mission: Contribute to innovative approaches in predicting, preventing, and curing diseases
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to better understand and characterize variability of water at the land surface - i.e. in soils, snow and groundwater - to help in predictions of future water availability, global water cycle dynamics and sea