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and interventional datasets in high dimensions. Our interdisciplinary approach integrates interpretable machine learning, statistics, algorithms, and single-cell multi-omics. Position Overview You will
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biogeochemical model using times series forecasting and machine learning. The Post Doc will focus on one or two of the questions depending on their expertise and interest. Minimum Acceptable Education & Experience
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of the benefits that you are eligible for, including health insurance, retirement plans, and paid time off. To access this tool and learn more about the total value of your benefits, please click on the following
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modeling are applied. To learn more about the lab: https://www.mdanderson.org/research/departments-labs-institutes/labs/xufeng-chen-laboratory.html The incoming fellow will receive training and conduct
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/10.1126/science.adm8203. 2. Keleş, M.F., Sapci, A.O.B., Brody, C., Palmer, I., Mehta, A., Ahmadi, S., Le, C., Tastan, Ö., Keleş, S., and Wu, M.N. (2025). FlyVISTA, an Integrated Machine Learning Platform
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 11 hours ago
/or machine learning/artificial intelligence algorithms. Projects may also include work focused on the analysis of spatial and geographic data and work extrapolating results to different spatial scales
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engineering, materials science and engineering, or computer science and engineering. The postdoctoral fellow is expected to actively work with more than one CQISE faculty member at OSU to strengthen
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[1] https://www.khaleejtimes.com/news/uae-health/one-in-every-hour-gets-stroke-in-uae Essential Qualifications: Electrical or computer engineering degree (or equivalent) Good written and spoken Arabic
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strong interest in machine learning and deep learning algorithms · Research and publication experience in health outcomes research Application Materials: · CV · Cover letter · Contact information for three
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-based knowledge with machine learning. You will work closely with the Utrecht University team and OpenGeoHub together with other project partners, to develop and implement surrogate and hybrid modelling