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- Yale University - Department of Genetics
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Field
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of academic research papers. Candidates are expected to demonstrate strong academic performance during their undergraduate and graduate studies, as well as the ability to conduct high-quality independent
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We are seeking a motivated postdoc to join our growing team in the Yale University Department of Genetics. Our lab uses experimental and computational methods to investigate cellular and health
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open questions at the interface. Essential Responsibilities Perform experiments, analyze, visualize, and interpret data Perform computational work and mathematical modeling Presentation of work in
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-generation permanent magnets using a powerful combination of high-throughput computation, machine learning, rapid synthesis and efficient characterization. This position requires expertise in synthesizing and
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. This position offers the opportunity to lead impactful research at the interface of aging biology, neurodegeneration, and spatial omics. The successful candidate will contribute to high-profile projects
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human omics (preferably related to neurodegeneration) studies is expected. Solid skills in at least one programming language (R, Python or Perl) and experience working in Linux and/or high-performance
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to test system performance Knowledge, Skills, and Abilities Computer skills sufficient to work with locally-written MatLab software and commercial software for data acquisition and interpretation
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seeking a highly motivated Postdoctoral student with experience in molecular biology, biochemistry (preferably of membrane proteins) and quantitative biophysical and/or computational approaches to examine
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access to the nation's leading user facilities for high-performance computing and scientific networking. The project will also closely synergize with the QUANT-NET program (https://quantnet.lbl.gov
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oncologic paradigms. Supported by in-house computational biologists and top-tier institutional core facilities, your primary mandate will be high-level hypothesis generation, designing and executing complex