117 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Stanford University
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collaborative culture. The Division of Pain Medicine is at the forefront of innovation in pain research, education, and patient care. Our postdoctoral program has successfully transitioned fellows
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resulting from T regulatory (Treg) cells by conducting genetic screens to overcome this suppression and to enhance CAR T cells for lymphoma. On the other hand, we also seek to apply the lessons learned from
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. Research Themes and Projects: We are an interdisciplinary research team integrating single-cell and spatial genomics, lineage tracing, synaptic proteomics, functional perturbation screening, and machine
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, minimal residual disease (MRD) detection, and the multi-omic characterization of various cancer types. Required Qualifications: PhD in related fields such as computational biology, cancer biology, and
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embryos This Human Frontier Science Program (HFSP) (link is external) funded project is in collaboration with the labs of Hervé Turlier (CIRB-CNRS) and Chema Martin (Queen Mary University of London). We
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: Candidate must have a strong quantitative background, with a PhD in computational biology, bioinformatics or related field including bioengineering, computer science, statistics, or mathematics. Strong
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world to develop knowledge necessary to realize that vision. We look for the brightest minds in the natural sciences, engineering, materials science, policy, economics, and business who are interested in tackling
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(link is external) program and the initiative on Learning Differences and the Future of Special Education (link is external) ) gain partnership experiences with practitioners and policymakers (via
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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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would include: Co-developing a hybrid machine learning/process-based model of anaerobic digestion processes Performing techno-economic and lifecycle analysis of microgrids build around novel biogas-fueled