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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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models, which are essential for understanding climate change impacts. The work involves reviewing existing modeling and model–data fusion techniques, and developing faster, machine-learning–based tools
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Simulation – Data Analytics and Machine Learning (IAS-8) at Forschungszentrum Jülich, which is dedicated to pushing the boundaries of data science theory and application. Our research spans from use-inspired
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Posting Title Graduate PhD Student (Year-Round) Machine Learning Applications for Cyber-Physical Power System Operations Intern . Location CO - Golden . Position Type Intern (Fixed Term) . Hours Per
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Your Job: We are looking for a PhD student to contribute to the development of fast, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular
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AI-powered digital modelling. The project aims to deliver battery-free micro‑power solutions for smart, climate-responsive urban infrastructure. The Research Assistant will play a central role in
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development opportunities, and a competitive benefits package designed to support your career and well-being. Job Description The AI, Learning and Intelligent Systems (ALIS) Group in the NLR Computational
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study in mathematics, statistics, machine learning, or science that will establish eligibility for PhD study. Applications close on 28 November each year. For more information and to apply visit
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objectives: 1 – Development of a tool for identifying operating regimes using machine learning techniques. 2 – Development of a tool for identifying the causes of process eco-efficiency degradation using
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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