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Midlands Graduate School Doctoral Training Partnership | Leicester, England | United Kingdom | 3 months ago
the precise mechanisms that drive addictive behaviour. The project will combine formal modelling, experimental simulations of socioeconomic gradients, and self-reported measures of early life
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validation tests. 2. Digital Modelling & Data Analysis Support development of the AI-enabled simulation and digital model platform. Run thermal–electrical models (COMSOL, ANSYS, or equivalent). Analyse field
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simulation and network-based methods for epidemiological modeling. ● Develop reinforcement learning models for decision-making during epidemic outbreaks. ● Documenting the entire process and all the codes
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to model development, experimentation, data processing, and performance evaluation as part of an active research environment. Responsibilities Implement and experiment with computer vision models using
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. The position involves close collaboration with experts in cardiovascular simulation and Scientific Machine Learning. Your tasks: Development and comparison of data driven models for the prediction of stresses in
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geometries. Current simulation-based approaches require complex 3D meshes and are often too slow for practical medical use. This project aims to create accurate and rapid surrogate models by combining physics
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Your Job: At the Institute for Advanced Simulation – Data Analytics and Machine Learning (IAS-8) we are looking for a PhD student in machine learning to work within a project linked to the
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infrastructure. The research will employ a combination of semiconductor device modelling, semiconductor simulation (by imec), electrical/optical characterisation, and system-level simulation of link performance
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the Department of Chemistry to develop innovative strategies for generating Machine Learning Interatomic Potentials (MLIPs) that accurately capture the dynamic nature of metal-ligand interactions. These models
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, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular geometries. Current simulation-based approaches require complex 3D meshes and are often too slow