120 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Stanford University
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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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or Chemical Engineering. • Prior work experience in hands-on laboratory experimentation. Prior work in microfabrication, engineering design (computer-aided design), and soft lithography. • Potential experience
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background in Computer Science, Informatics/Biomedical Data Science, Engineering, Statistics, Computational Biology, or a related field Prior experience in computer vision, with application of deep learning
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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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, 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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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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: 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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(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