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(100% FTE), 12-months/year, with an initial term appointment of ~4 years (48 months), renewable depending on funding and/or satisfactory performance. Start date The start date is negotiable and the
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Key Responsibilities: • Adapt internal computational pipelines to analyze high-dimensional patient datasets, including single-cell sequencing, spatial transcriptomics, clinical, and other relevant
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optimization problems—often NP-hard and extremely difficult to solve at scale. These problems arise in diverse, high-impact domains, including renewable energy management, healthcare resource allocation, and
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Science. Proficiency in programming (Python, Julia), and high-performance computing (provide evidence with specific examples) Ability to work independently and collaboratively. Strong written and oral
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research in ML for Health, including HIPAA-compliant compute infrastructure with high memory GPUs and access to Stanford Healthcare data, which includes EHRs for over 5M patients and 100M clinical notes
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) . Lab information can be found here: http://profiles.stanford.edu/nathan-lo (link is external) . Review of applications will be performed on a rolling basis and continue until the position is filled. Does
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omics to advance biological and clinical discoveries and develop next-generation theragnostics. The postdoctoral fellows will mainly focus on (1) creating novel computational algorithms to analyze and
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learning to derive principled models of cortical computation. Our newly refurbished primate facility, state‑of‑the‑art Neuropixels rigs, and high‑performance computing cluster offer an unmatched playground
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outstanding resource to apply biomedical statistical tools for data analysis for our groups' ongoing preclinical work and tissue assays. Perform RNA sequencing, including bulk sequencing, single-cell sequencing