55 phd-studenship-in-computer-vision-and-machine-learning Fellowship positions at University of Michigan
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clinically validate innovative predictive models utilizing AI and machine learning; test cadaveric anatomy study implementation Complete Data collection and finalize evaluation of AI predictive models
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in the position and outline skills and experience that directly relate to this position. Job Summary The lab of Zhong Wang, PhD housed within the Department of Cardiac Surgery is seeking a self
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information from electronic health records Conduct systems biology research and analysis for high dimensional data Required Qualifications* PhD degree or higher in computer science, biomedical informatics
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/epigenetic cancer research are encouraged to apply. This position is ideal for a recent (or soon to be) PhD graduate who is interested in strengthening their experimental, analytical, and publishing skills
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programming is a plus. Required Qualifications* Ph.D. degree in biomedical engineering, medical physics, electrical engineering, computer science, neuroscience, or equivalent disciplines. Good written and
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employees and our vision is to attract, inspire, and develop outstanding people in medicine, sciences, and healthcare to become one of the world’s most distinguished academic health systems. In some way
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particular sequential, multiple assignment, and randomized trial design. You will have the opportunity to mentor the PhD students on the team. Experience in any of the following areas may be useful: SMART
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behaving mice, and advanced modeling + machine learning analyses. Please read more about our research at www.apostolideslab.org . Key questions we want to answer are: How do neural circuits extract
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environment Work Locations Work location is expected to be onsite in Ann Arbor with flexibility for remote work within our overall Center policies. Modes of Work Positions that are eligible for hybrid or mobile
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Apply Now Job Summary In this position, the candidates will learn strategies and techniques for LC-MS analysis, drug optimization and lead drug candidate selection, pharmacokinetics and drug