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accessibility), and clinical data. * Develop, apply, and benchmark machine learning and statistical models for subtype discovery, classification, and outcome prediction. * Contribute to the development
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date). Demonstrated research experience with quantitative ultrasound methods. Strong background in model‑based or physics‑informed machine learning, with experience developing algorithms that integrate
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. The researcher is expected to have (i) strong machine learning skills to improve model performance and robustness, and (ii) exemplary passion and motivation to pursue multidisciplinary research at the intersection
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position focused on advancing crop production and physiology through field experimentation, computation (statistics and machine learning), and process-based models. The successful candidate will have an
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Intelligence and Machine Learning. Join a team of scientists at the leading macromolecular crystallography beamlines, the crystallization laboratory, the computing center and contribute to science projects
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collaboration with industrial and public-sector partners. We expect the candidate to publish in competitive machine learning, software engineering and security venues, such as Neurips, ICML, ICLR, ICSE, FSE, ASE
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the intersection of machine learning (ML) and the sounds of wildlife (“bioacoustics”). We are also happy to consider candidates in one of the two fields who can demonstrate a strong basis for working in this cross
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large language models (LLMs), natural language processing (NLP), and/or machine learning, with a verifiable track record (e.g., publications, thesis, or open-source contributions). Proficiency in Python
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are especially encouraged: (1) statistical mechanics, condensed-matter theory, quantum field theory; (2) tensor networks, quantum information, quantum algorithms; (3) machine learning, generative models, large
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-based methodologies for protein structure prediction and cofolding Machine learning guided virtual screening Taking on organizational tasks and writing grant applications Your Profile The ideal