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has been created in theoretical quantum computing. We are particularly interested in candidates working on quantum error correction, algorithms, simulation and qubit architectures. We are also
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interdisciplinary work, for example in medicine or life sciences, who address key issues in AI such as reproducibility, safety, trustworthiness and robustness, and who engage with the theoretical and algorithmic
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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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