122 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Stanford University
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, robust, and reproducible data analysis. Conventional statistical approaches will be combined with innovations in interpretable machine learning to address each aim from multiple angles. Analysis code will
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principals to problem solve work. ● Ability to maintain detailed records of experiments and outcomes. ● Ability to quickly learn and master computer programs, databases, and scientific applications
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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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: 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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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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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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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
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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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clinical shadowing experiences. Research topics range from machine learning, designing, and evaluating clinical decision support content to disintermediate scarce medical consultation resources, evaluating