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Field
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frameworks such as GTSAM, G2O, or similar; computer vision frameworks like OpenCV; and/or deep learning frameworks such as PyTorch and TensorFlow Prior experience with industry or publicly funded research
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Measure Theory : Leveraging foundational mathematical frameworks to design robust modeling approaches. 2) Deep Learning : Exploring cutting-edge techniques such as multimodal data integration, diffusion
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equitable research environment that values diversity in all its forms. To learn more about ongoing research and recent publications, please visit: https://kaushiklab.com . Why MUN? The Faculty of Medicine
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U.S. Department of Energy (DOE) | Washington, District of Columbia | United States | about 2 months ago
of the DOE. As a result, fellows will gain deep insight into the federal government's role in the creation and implementation of energy technology policies; apply their scientific, policy, and technical
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 months ago
: * Introductory proficiency in Python, R, or another programming language * Prior exposure to machine learning or AI techniques in clinical research * Experience using deep learning frameworks * Familiarity with
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-based sensor data to enhance the prediction of peatland soil properties and functions. You will focus on leveraging machine learning/deep learning techniques along with explainable artificial intelligence
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, and use deep learning to gain insight into biological processes. You will also gain direct exposure to cardiovascular physiology and rodent imaging in close collaboration with biologists. We work
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-based sensor data to enhance the prediction of peatland soil properties and functions. You will focus on leveraging machine learning/deep learning techniques along with explainable artificial intelligence
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). Team player and great collaborator Strong interest in interdisciplinary work at the interface between dementia/ neurodegeneration, modeling, and machine learning Prior experience in deep learning, or/and
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materials property predictions. A deep understanding of materials properties and close connections in academia and industry enable the group to explore exciting research avenues. For more information about