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will work with Dr. Baladandayuthapani and his research group in developing statistical and computational methods for large-scale, high-dimensional, complex-structured biomedical data. The research topics
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. Additionally, the candidate should have strong qualitative research skills and be familiar with quantitative methods. Research responsibilities will include designing a study, collecting and analyzing data
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manufacturers need to trust AI tools to improve production, quality, and safety. The M-CAML (Michigan Cognitive and Adaptive Manufacturing Lab) team aims to develop methods and tools for interpreting and
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methods to generate conclusions based on data. 4. Assist in preparing and writing technical reports, peer-reviewed conference and journal publications; and, present results at local and national engineering
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alongside a team of well qualified predoctoral fellows. Apply and advance econometric methods. Propose new projects and develop grant proposals. This position is ideal for an applicant with a deferred
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. The lab utilizes a range of interdisciplinary and multi-method approaches including advanced statistical modeling, laboratory-based stress paradigms, ecological momentary assessment, and biomarker analysis
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engagement. Demonstrated success in publishing refereed journal articles. Interest in applying AI methods to environmental and sustainability issues. Modes of Work Positions that are eligible for hybrid
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these methods accurately and independently, interact effectively with computational biologists, and clearly communicate findings through presentations and publications. Candidates with strong expertise in
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the laboratory will also have a strong translational component including involvement in an ongoing clinical trial. The ideal candidate will possess extensive experience in molecular and cellular biology and will
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with Owen-Smith and other members of the IRIS team. Familiarity and demonstrated expertise with network and computational science methods, science of science research, creation and manipulation of large