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for healthcare. The Alsentzer Lab is an interdisciplinary research group in the Department of Biomedical Data Science at Stanford University. Our mission is to leverage machine learning (ML) and natural
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multidisciplinary team comprised of fellow postdoctoral appointees, experimentalists, and staff scientists, with computational fluid dynamics (CFD) and artificial intelligence/machine learning (AI/ML) expertise, with
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and/or Work Experience M.D. or PhD Required Qualifications M.D. or PhD Required Certificates/Credentials/Licenses M.D. or PhD Required Computer Skills N/A Supervisory Responsibilities Yes Required
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; maintain research databases. Prepare manuscripts, grant applications, and conference presentations. Learn and perform lab-specific techniques; supervise and mentor junior team members. Seek extramural
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Sumter enable students to earn associate or bachelor’s degrees through a combination of in-person, online or blended learning. All of our system institutions place strong emphasis on service — helping
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machine learning are desirable, applicants from other quantitative fields (e.g. math, physics, statistics, computer science) who are eager to learn about neuroscience are highly encouraged to apply as well
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compensation package. This tool takes into account all of the benefits that you are eligible for, including health insurance, retirement plans, and paid time off. To access this tool and learn more about the
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theory, multi-objective optimization and machine learning. The specific project aims to understand the multiscale interactions shaping human gut bacteria and human gut pathogens. The project will combine
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). What We’re Looking For: DVM and/or PhD (or equivalent) in veterinary, animal, or biological sciences. Experience or strong interest in: Udder health and animal welfare Digital learning and employee
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for the development and implementation of innovative statistical and machine learning approaches for analysis of genetics and genomics data. We are particularly interested in applicants who have experience with methods