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Field
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Bayesian inference, likelihood-free inference, uncertainty quantification, identifiability analysis, or scientific machine learning. Strong programming skills (Python, Julia, Matlab, C++, or similar). Strong
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intelligence. Our research spans computer vision, machine learning, and natural language processing, focusing on multimodal learning, data fusion, spatial-temporal modeling, and vision–language models. We study
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identify the cells, cellular states, and biological programs through which genetic variation influences disease. Your work will sit at the intersection of statistical genetics, machine learning, and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 9 days ago
world. Position Summary This Postdoctoral Research Associate will conduct advanced research in artificial intelligence, machine learning, computer vision, and medical image analysis. The position will contribute
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
world. Position Summary This Postdoctoral Research Associate will conduct advanced research in artificial intelligence, machine learning, computer vision, and medical image analysis. The position will contribute
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characterization (PXRD and Rietveld refinement, SEM, XPS, etc) Experience with machine learning techniques, particularly machine-learned interatomic potentials, in the context of chemical research. Programming
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other field or laboratory instrumentation Apply appropriate behavioral, statistical, econometric, causal, spatial, machine learning, deep learning, computer vision, time-series, or mixed-methods
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Experience developing software with Python, R, and/or Julia Experience with machine learning and AI algorithms and tooling (e.g. PyTorch) Experience with remote sensing and open-source GIS tools (e.g. Google
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, GIS tools and quantitative/spatial approaches including machine learning to understand how wildlife species are reacting to climate change at continental or global scales. Specific project ideas could
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its treatment options in patients with OSA. Newly NIH funded projects also include mathematical modeling and machine learning of models of tissue hypoxia using sleep studies and molecular markers