89 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Stanford University
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will have connections to both the Molecular Imaging Program at Stanford (MIPS) and the Radiological Sciences Laboratory (RSL). The ideal candidate for this position will have interest in being trained in
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Required Qualifications: PhD in Neuroscience, Bioengineering, Electric Engineering, Computer Science, Physics, or a related field Strong quantitative and analytical skills Experience in either experimental
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and attention to detail; Proven technical and analytical skills; Ability to troubleshoot an experiment as necessary. Proficiency with a computer; Knowledge of math and statistics, experience with PRISM
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fellowships and NSF SBE postdoctoral awards. We especially welcome applicants with theoretical interest in child language development, strong computational and analytical skills (deep learning frameworks), and
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Qualifications: A PhD in biology, neuroscience, development, or a related field. At least one first-author publication. Experience with iPS cells is preferred but not mandatory. Required Application Materials: CV
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clinical shadowing experiences. Research topics range from machine learning, designing, and evaluating clinical decision support content to disintermediate scarce medical consultation resources, evaluating
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approaches to remove atmospheric particulate (e.g., PM2.5) pollution. The math-based subgroup focuses on the use of deep learning and generative AI to address critical problems for the electric grid and broad
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the Education Data Science program) develop cultural competencies (via events organized by the Race, Inequality, and Language in Education program and the initiative on Learning Differences and the
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. Required Qualifications: PhD in Computer Science, AI/ML, Computational Biology, or a related quantitative field. Proven expertise in deep generative modeling and large-scale multimodal learning. Experience
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and patient-reported outcomes; (b) observational research and comparative effectiveness studies; (c) intervention studies; (d) clinical informatics, mobile/electronic health; (e) machine learning