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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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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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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 foundation. Candidates are also expected to have strong coding and implementation skills, with the...
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reinforcement learning for large language models (LLMs). Research directions include developing next-generation post-training algorithms, exploring diffusion-based approaches to reasoning with language models
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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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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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guidance and robotics. Our work combines medical imaging, computer vision, and machine learning with strong clinical translation, in close collaboration with Balgrist University Hospital and the national
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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