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and creative in performing studies to both make children comfortable while still obtaining high quality data. Experience with computer coding (in Matlab or Python) is highly preferred and a willingness
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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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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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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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. 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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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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) and knowledge in biostatistics are required. Experience working with genetic association studies, immune repertoire, single cell and multi-omics data analysis, and a high-performance computing
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Appointment Term: Initial appointment is for 18 months, with the possibility of renewal based on performance and funding availability. Appointment Start Date: 11/1/2024 Group or Departmental Website: http
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of approaches to understand diseases like cancer and Alzheimer’s Disease from a multi-scale systems perspective that integrates novel technologies with advanced experimentation and computation to understand
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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