52 condition-monitoring-machine-learning Fellowship positions at University of Michigan
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of reagents and supplies; optimize ordering and project scheduling to meet service expectations. Monitor instrument performance, oversee routine maintenance, and coordinate vendor support. Uphold SOPs, aseptic
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great strides in better understanding these common, debilitating musculoskeletal conditions. There is strong potential for development in technical expertise in addition to improvements in communication
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, evidence-based care for musculoskeletal injuries and conditions. Our faculty, staff, and trainees conduct a diverse range of musculoskeletal research and scholarship. The department?s research program is
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. Establish xenograft and orthotopic tumor models. Develop tumorigenic models using GEMMs (Genetically Engineered Mouse Models). Monitor mice for tumor development and other experimental endpoints. Administer
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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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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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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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particular interest in developing and testing low-cost technologies for the assessment and treatment of neurological and orthopedic conditions. The postdoctoral fellow will be expected to contribute
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, including the most recent papers. Preference may be given to candidates with knowledge in machine/deep learning, statistical inference, image analysis, survival analysis, causal inference, and high
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have exceptional resources to facilitate research including access to administrative, research, and computer support staff. Required Qualifications* PhD degree or equivalent in epidemiology, gerontology