60 parallel-computing-numerical-methods Postdoctoral research jobs at Stanford University
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backgrounds trained in chemistry, chemical biology, microbiology, and/or biophysics fields. We have launched a collaborative antibacterial drug design program integrating chemical biology and mechanistic
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protein engineering techniques. In parallel, we are also looking for postdocs interested in developing high-throughput screens for single-domain antibodies, called nanobodies, that perturb intracellular
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transmission modeling, statistical modeling, spatial data analysis, and cost-effectiveness analysis. In parallel, we conduct research on vaccine-preventable infections, developing and evaluating predictive
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preparation and dissemination of findings at national and international conferences. Collaborate with investigators across rheumatology, pain medicine, biostatistics, informatics, and behavioral science
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methods to improve prediction model generalizability, model fairness, and generalizability of fairness across different clinical sites. The researcher will have the opportunity to use machine learning and
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The Stanford Abdominal Diffusion Group is seeking thoughtful, motivated and collaborative postdoctoral fellows to join a growing team developing motion-robust multi-shot DWI methods for liver, pancreatic, and
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common equipment, and also have the benefit of access to research facilities at Stanford University including core computing, microscopy, library, biostores, and analytical facilities. The Spin lab has
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of the selected candidate, budget availability, and internal equity. Pay Range: $80,000-95,000 The Alsentzer Lab at Stanford is seeking a postdoctoral fellow to advance trustworthy, deployable AI methods
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postdoctoral fellow with interest in organic chemistry and radiopharmaceutical development. Successful candidates will join the Molecular Imaging Program at Stanford within the Department of Radiology, Stanford
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. Expertise in computational neuroscience software (e.g., MATLAB, Python) as well as statistical methods and statistical packages (e.g. SAS, R). Experience with machine learning methods is preferred