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external) How to Submit Application Materials: To begin the application process, please send an email using the subject line “Postdoctoral Position in Machine Learning for Advancing Mental Health” to Tina
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for healthcare. The Alsentzer Lab is an interdisciplinary research group in the Department of Biomedical Data Science at Stanford University. Our mission is to leverage machine learning (ML) and natural
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nano-mechanics, and machine learning as it applies to the field of computational mechanics. Candidates will be given opportunities to develop their teaching experience by designing and teaching a class
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, or machine learning experts to create predictive virtual 3D mammalian embryos for human health, especially congenital heart diseases. We welcome applicants with expertise in genomics, developmental biology
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not required as part of this position. Required Qualifications: Strong mathematical background, including expertise in one or more of the following areas: machine learning, statistics, and algorithms
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understanding of neuroscience but also advanced technical expertise in machine learning, artificial intelligence, and data modeling approaches. Responsibilities: Conduct research on the mechanisms underlying
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our team. We are looking for postdoc candidates to develop and apply cutting-edge technologies in spatial transcriptomics, single-cell sequencing, machine learning, and functional genomics
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Cancer Registry, as part of the national SEER registries. The postdoc fellow will work closely with statisticians, computer scientists, oncologists, and epidemiologists in the lab and other collaborating
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include, but are not limited to, using the latest computational learning-driven approaches, including computational social science, foundation models and multimodal machine learning, to enhance
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focus on machine learning in the Stanford Center Cancer Cell Therapy at Stanford University School of Medicine. We seek a highly creative and motivated scientist to perform cutting-edge computational