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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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Your profile PhD applicants must possess a Master's degree in mathematics, theoretical physics, or computer science. Candidates should have an exceptional academic record and a robust mathematical
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applications, including solving mathematical reasoning problems and tackling the Abstraction and Reasoning Corpus (ARC) challenge among others. The ideal candidate has a strong background in machine learning and
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opportunities to exchange ideas with other doctoral students, receive feedback on the work in progress, and acquire the skills necessary for an academic career. The Doctoral Program also supports its members with
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opportunity to learn, develop and apply a range of cutting-edge modeling and computational techniques. You will work in an interdisciplinary, cutting-edge, fast-paced research environment, interact with
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part of this program, IOB offers PhD and MD-PhD fellowships to outstanding candidates from diverse fields such as Biology, Medicine, Physics, Computer Sciences and Engineering who wish to pursue
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University of Basel, Condensed Matter Theory and Quantum Computing Position ID: University of Basel -Condensed Matter Theory and Quantum Computing -PHD [#21542] Position Title: Position Type
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changes in station or vehicle configurations on travel behavior. The doctoral student will further estimate causal effects through predictive machine learning models, and develop a generalizable decision
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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real