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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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recently completed a pilot study investigating the use of atmospheric pressure ionisation mass spectrometry coupled with machine learning for differentiating between brain tumours and normal tissue
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Your Job: We are looking for a PhD student in machine learning to work within a project linked to the “Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE)”. Your Job: Develop 3D+t
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the use of large language models to support neural network design and data preprocessing. The position involves close collaboration with experts in cardiovascular simulation and Scientific Machine Learning
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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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, network analysis, or machine learning are a plus Good organisational skills and ability to work both independently and collaboratively Effective communication skills and an interest in contributing to a
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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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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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-based learning opportunities for instructors through asynchronous resources, workshops, and programs; 2) University-wide collaboration with partners in the faculty development ecosystem; 3) Center
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machine learning (ML) along with data from previously solved problem instances to solve new, yet similar, instances more efficiently than with general purpose algorithms such as Netwon`s method. In