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, biologics, and cannabis. Apply statistical and machine learning approaches (e.g., sequence analysis, latent class analysis, clustering) to examine medication use trajectories and patient subgroups
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some flexibility possible Group or Departmental Website: https://bodymri.stanford.edu/ (link is external) How to Submit Application Materials: To apply: Send the required application materials to Leslie
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Resilience Lab is seeking a full-time or shared Postdoctoral Research Fellow to play a leadership role in an exciting NIH-funded study investigating the effects of cannabis use among college-aged youth
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in Neuroscience, Biomedical Engineering, Computational Biology, or a related field. Strong background in signal processing, including neuroimaging and/or electrophysiology (EEG, MEG) data analysis
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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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Biology, Bioinformatics, Computer Science, or a related field • Strong programming skills in R and/or Python • Experience with analysis of single-cell sequencing data • Familiarity with spatial
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variety of simulation and optimization techniques. Key areas of interest may include control theory, robust optimization, or distributed optimization. 2. The second candidate will focus on applied research
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. Prevalent TCR clones will undergo reverse engineering to deduce the peptide bound, and this information used to generate MHC tetramers to study the induction of these clones during the anti-tumor response
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opioid research. Fellows will play a pivotal role in NHLBI, NIAMS, and NICHD-funded studies, focusing on the intersection of perioperative pain, sleep, and opioid use. Fellows will be exposed
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cognitive abilities, and how those developmental trajectories differ (and are similar) across contexts. Planned projects: The planned research will focus on analysis of data collected in a global multi-site