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at the interface of machine learning, statistics, and live-cell biology. The position is co-supervised by Prof. Olivier Pertz (Cell Biology) and Prof. David Ginsbourger (Statistics), and the student will be equally
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learning, non-Hermitian systems The Quantum AI lab at ETH (Prof. Juan Carrasquilla ) invites applications for PhD positions to work at the intersection of computational quantum many-body physics, machine
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funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description PhD Position in Energy-Efficient Machine Learning for Wearable and Augmented Reality
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optimization – with rigorous theoretical analysis. The ideal candidate has strong machine learning and AI expertise and is comfortable with – or eager to learn – large-scale multi-GPU experimentation
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systems, and space applications. We combine theory, physics-based simulations, machine learning, and autonomous workflows to understand and design materials that can perform under conditions where
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combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real-world energy applications, the project aims to better capture the dynamics of urban infrastructures
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Your profile PhD applicants must hold a Master's degree in computer science, mathematics, or electrical engineering, with demonstrated strength in either practical implementation or theoretical foundations. Candidates should possess an exceptional academic record and a strong mathematical...
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transfer, developing and employing computer simulations, laboratory experiments, and field analyses. Our aim is to gain fundamental insights and develop sustainable technologies to address societal needs
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essential, while experience with machine learning is advantageous but not strictly required. Excellent English skills, both in verbal and written communication, are required for the project. We are looking
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secondments at UCL – University College London and at University of Leeds (UK). We are looking for you! Do you want to be trained to develop multi-level thrombosis risk prediction models by integrating insights