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from varied sources, and machine learning methodologies Required Application Materials: 1. A cover letter describing: a. Your interest in this position b. Your relevant training and experience c. Your
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to) the qualifications of the selected candidate, budget availability, and internal equity. Pay Range: $86,100 Aligning Machine Learning Models with Algorithmic Reasoning Tasks We are seeking a postdoctoral researcher to
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focuses on translational research at the intersection of bioelectronics, healthcare-focused nanofabrication, and emerging applications of machine learning in radiology. Our team operates within a state-of
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transcriptomics analysis • Interest in cancer biology and immunology principles • Excellent written and verbal communication skills Preferred Qualifications: • Experience with machine learning approaches
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research on computational linguistics, machine learning, practical applications of human language technology, and interdisciplinary work in computational social science and cognitive science. The Stanford
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particular, the postdoc will focus on applying reinforcement learning to discover vulnerabilities and failure modes in software systems that support critical infrastructure, in particular AI-based decision
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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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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