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Field
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approaches for hydrologic predictions, risk analysis. Experience in working with big data from remote sensing is highly desirable. Strong computer programming skills and experience in working with High
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performance. The salary is commensurate with experience. Applications are invited from individuals who are interested in applying experimental psychology and Bayesian computational modeling to understanding
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understand, respect and can contribute to the University’s mission and values. Duties and Responsibilities: The successful candidate will: Conduct research on GSI maintenance and performance by installing and
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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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) molecular dynamics simulations; and 3) high-performance computing environments. Prior programming experience is preferred but not required. The postdoctoral scientist will design, perform, and develop new
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. Our lab works in the areas of ultrafast science, nanoscale thermal transport, and microelectronics, for applications in energy-efficient computing, thermal management, and energy conversion. We seek
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analyses. In addition, experimental and computational abilities for large datasets are highly desirable. The applicant must be able to comply with high standards of work ethics, regulations and rules
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will also be involved in different projects (e.g. USDA--soil health/microbiome, and funded NASA program). The major tasks will include but are not limited: 1. USDA--soil health/microbiome Perform soil
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relevant programming languages. Ability to use/learn several advanced modeling methods (e.g., statistical, mathematical, individual-based, or machine learning models). Experience with high-performance
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relevant programming languages. Ability to use/learn several advanced modeling methods (e.g., statistical, mathematical, individual-based, or machine learning models). Experience with high-performance