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crystal structures. Present your work at international conferences and learn about state-of-the-art methods in machine learning, explainable AI, counterfactuals and generative AI for material sciences. Take
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descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange isotherm parameters directly from molecular properties. These predictions will be integrated
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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 develop learning-based surrogate models for predicting stress fields in patient-specific arteries. Especially high stresses in plaque can lead to
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to be part of a unique and diverse community that works on high-impact research, educational, business, and societal problems. Position Summary The Machine Learning Researcher - Epidemiology will report
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Infrastructure? No Offer Description Work group: JSC - Jülich Supercomputing Centre Area of research: PHD Thesis Job description: Your Job: We are looking for a PhD student to develop learning-based surrogate
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heavily relies on empirical determination of key model parameters. By combining protein structure descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange
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this limitation in the use of satellite observations by make a direct use of radiance observations retrieved by satellites using machine learning without the need of radiative transfer calculations. The new model
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Supervisor: Professor Fernanda Duarte Start date: 1st October 2026 Applications are invited for a fully-funded DPhil studentship in Machine Learning Interatomic Potentials for Metal-Ligand
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PhD positions for highly qualified graduates Faculty: Faculty of Social and Behavioural Sciences Department: Social Sciences Hours per week: 36 to 40 Application deadline: 8 March 2026 Apply now