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methodology development as well as applied cancer bioinformatics in a variety of disease sites, including the incorporation of statistical, machine learning & QML ideas. Multiple collaborative opportunities
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, mathematics, or a related field.bioinformatics, statistics, mathematics, or a related field. Why Join Michigan Medicine? Michigan Medicine is one of the largest health care complexes in the world and has been
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Qualifications* PhD Degree in Engineering, Computer Science, Data Science, Applied Mathematics, Statistics, or a related field Familiarity with (biomedical) signal processing Experience working with clinical data
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the mathematical and statistical properties of the model in order to achieve higher agreement with biological assumptions and better interpretability. The candidate will have strong expertise in generative AI
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procedures and processes; Mentoring trainees working on the project in study design, research methods, scientific writing, and team science. Required Qualifications* PhD in epidemiology, statistics
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, operation of the scanner, data acquisition and analysis, image evaluation, and statistical analysis. The fellow will also be expected to prepare manuscripts and conference abstracts related to projects and
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Report and Rankings. We have a thriving body of 42 faculty, 9 postdocs, 273 students, and 123 research and administrative staff members and close ties with the Department of Statistics, the Institute
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and statistical modeling for reliable analysis on spatial multiomic data. The candidate will work on both probabilistic modeling, software development, and cancer biology analysis for this position
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, operation of the scanner, data acquisition and analysis, image evaluation, and statistical analysis. The fellow will also be expected to prepare manuscripts and conference abstracts related to projects and
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blot, immunofluorescence) Cell transfection Mechanical testing and biomaterials characterization Quantitative image analysis Computational skills including data processing and statistical analysis Prior