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. The PhD position will focus on developing a deep-learning algorithm for analyzing the acquired experimental data. The PhD position will focus on development a comprehensive and AI-driven platform
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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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position (technician) will focus on performing Raman/FTIR on retrieved samples. The PhD position will focus on developing a deep-learning algorithm for analyzing the acquired experimental data.
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position (technician, this position) will focus on performing Raman/FTIR on retrieved samples. The PhD position will focus on developing a deep-learning algorithm for analyzing the acquired experimental data.
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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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the theoretical and algorithmic foundations of AI. A strong commitment to excellence in undergraduate and graduate teaching and mentorship is essential. Preference will be given to candidates who show promise in
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correction, algorithms, simulation and qubit architectures. We are also interested in outstanding scientists in theoretical quantum computing in a broader sense. A primary research direction is to include spin
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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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single electron spins in diamond as sensors to explore magnetic phenomena at the nanoscale. This doctoral project will center around the development and application of scanning magnetometry at ultra-low
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probe microscopy. Our research focuses on using single electron spins in diamond as sensors to explore magnetic phenomena at the nanoscale. This doctoral project will center around the development and