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will involve investigating the physiological and functional variation of plants using spectroscopy and will include greenhouse work, field work, plant phenotyping, computational analyses of hyperspectral
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building trusting partnerships between the institute's two schools and across the University of Michigan. Our postdocs approach this work with a commitment to the University of Michigan's values in mind
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agents, including uncertainty quantification at the agent’s level. The project will bring together ideas from Statistics, Probability, Statistical Machine Learning, Statistics and Game Theory and is an
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areas. You will also work to develop a programme theory and with the research team to prepare a future research proposal. You will be working as part of a larger research team, and with HDRC partners in
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allows the Postdoc to pursue research as a vital component of his/her educational mission and professional career development. Works under minimal supervision with some latitude in the use of initiative
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thus expect the postdoc to take an independent role to conduct studies that are broadly related to the project, to conduct high-quality research within the context of the project, and to contribute
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curious to deliver work that matters, your journey starts here! Innovation. Interdisciplinary collaboration. Complex problem solving. In Carnegie Mellon University's Department of Mechanical Engineering
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 2 months ago
Work Schedule Monday thru Friday Pay Rate Minimum Experience/Education This position requires a PhD in Biology, Genomics, Computer Science, or a similar field. The postdoc will report to both the NCRC in
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Assistant / Research Technical Assistant / Project Technical Assistant / Research Administrative Assistant / Project Administrative Assistant (Full-time/Part-time) (Ref. 250509009) (1) Senior Research
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quest of discovering principles of natural adaptive computation and fundamental understanding of cells – the building blocks of life we use a blend of computational, experimental, and theory-driven