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analysis Machine learning and retrieval-augmented AI models for biomarker prioritization and decision support ·Work closely with cross-functional team members to develop hypotheses, interpret data, and
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Fellow to develop and evaluate artificial intelligence methods for physical medical procedures. The fellow will design and implement machine learning models to analyze procedural data, support clinical
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. Ideal candidates will have demonstrably strong research skills, evidenced by multiple publications in top-tier machine learning or artificial intelligence conferences and/or leading scientific journals
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Successful candidates will have publications in information theory and machine learning venues, such as IEEE Transactions on Information Theory, ISIT, NeurIPS, ICML, ICLR, and ACM FAccT. Experience in machine
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Informatics (DBMI) at Harvard Medical School and the Yu Lab are seeking a Postdoctoral Research Fellow with experience in machine learning and scientific programming. The candidate will work with a multi
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. transformer models). One focus of this work will be on B-cell receptor evolution. Experience in applications of modern machine learning methods as well as in biological data analysis are needed for the position
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focusing on multi-omic integration analytics, machine learning, and/or AI. In addition to carrying out research, the successful candidate will be expected to apply for fellowship funding, contribute
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on projects at the intersection of computational neuroscience and machine learning. This position is part of a multi-investigator grant on the role of memory in intelligence systems. The Postdoctoral Fellow
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position in biomedical informatics is available at Harvard Medical School to work at the intersection of advanced machine learning and large-scale biomedical data. The selected fellow will join a dynamic
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protein coding genetic association data with functional and machine learning-derived features 4. Developing methods to characterize the genetic architecture of autism Salary and Benefits This position is