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surveillance and preparedness planning using multiple modeling approaches. The successful candidate will develop and implement statistical and machine-learning models, integrate multi-source ecological datasets
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. Emphasis is placed on artificial intelligence/machine learning approaches applied to digital data and multi-omics data. Additional responsibilities include mentoring students, collaborating with faculty
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landscape constrains or enables discovery. The project draws on tools from topological data analysis (e.g., persistent homology, Euler characteristic curves, discrete curvature), machine learning (e.g
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applications. Perform experiments, data analysis, interpretation, and presentation. Report progress regularly. Write manuscripts and grant applications. Qualifications Requirements Completed PhD and have
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: A recent PhD (or MD) degree in a relevant discipline, such as decision science, health services research, epidemiology, applied mathematics, or industrial engineering. Expected graduation in Spring
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Chekouo and his collaborators within and outside the University of Minnesota. The research will focus on the development of Bayesian statistical/machine learning methods for the data integration analysis
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temporary or permanent basis for any reason at any time. Qualifications Required Qualifications • Has a PhD in health sciences or a related field • Led nutrition-related projects • Expertise in data