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, public health, biostatistics, biomedical sciences, engineering, mathematics, statistics, or a related field. Experience or coursework involving artificial intelligence, machine learning, natural
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programming languages. Experience with DICOM data, medical-image registration, high-performance computing, or GPU-based computation. Familiarity with machine-learning or deep-learning methods for medical-image
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novel findings that inform disease etiology. The candidate should be interested in focusing on multi-omic integration analytics, machine learning, and/or AI. In addition to carrying out research
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for cell transplantation therapies in animal models of Alzheimer's disease, stroke, and epilepsy. To achieve this goal, the candidate will combine a gene network-based approach with a machine learning model
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in AI and machine learning (e.g. NeurIPS, ICML, ICLR, or similar) and/or publications with clear AI contributions in leading biomedical journals (e.g. Nature Genetics, Nature Medicine, Nature Machine
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record in top-tier venues in AI and machine learning (e.g. NeurIPS, ICML, ICLR, or similar) and/or publications with clear AI contributions in leading biomedical journals (e.g. Nature Genetics, Nature
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machine learning (e.g. NeurIPS, ICML, ICLR, or similar) and/or publications with clear AI contributions in leading biomedical journals (e.g. Nature Genetics, Nature Medicine, Nature Machine Intelligence
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biosignals. Application of machine learning techniques for classification of different classes using the extracted features. Assembly, documentation, testing, and use of an innovative biosensing system
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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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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