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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
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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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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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machines that both learn from humans and help humans learn. The postdoctoral fellow will lead a project using AI technologies to support active learning in young children, by empowering them to create
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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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it; as well as have theoretical skills including algorithm implementation/development and data visualization. Experience and interests include designing machine learning pipelines, building web
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(PhD, MD, or equivalent) conferred by the start date. Proven research and/or professional experience in machine learning and/or natural language processing, with a preference for prior experience working
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neuroscience software (e.g., MATLAB, Python) as well as statistical methods and statistical packages (e.g. SAS, R). Experience with machine learning methods is preferred. Demonstrated experience with large
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decision making systems, in particular the use of different optimization, machine learning, and decision making modeling techniques for problem solving. Desire to grow collaborative research and mentorship
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are not limited to, using the latest computational learning-driven approaches, including computational social science, foundation models and multimodal machine learning, to enhance the development