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. Proficiency in machine learning, digital twin technology. Previous peer-reviewed publications. Programming skills. Ability to conduct independent research in a multi-disciplinary team. Point of Contact Jordan
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National Aeronautics and Space Administration (NASA) | Hampton, Virginia | United States | 20 days ago
the computational work. Researchers in the group have experience with crystal plasticity using finite element and fast Fourier transform methods, fatigue indicator parameters, process modeling and machine learning
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 12 hours ago
. Examples include: determination of high-resolution shape models using shape-from-shading or novel machine-learning methods; studies of high-resolution shape models for geophysical and geological studies
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quality and consumer satisfaction. You will also apply statistical and machine-learning tools to explore how physical and chemical fiber parameters relate to dye uptake behavior, dyebath exhaustion, color
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machine learning, Computer vision, Swarms, Autonomous Robots, hardware security, and Embedded systems development is desired. The successful applicant will work on various projects on robotics and computer
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Job Description Applications are invited for a full-time Postdoctoral Research Associate position within the Bradley Department of Electrical and Computer Engineering, Wireless@Virginia Tech
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– Wrocław University of Science and Technology offers employees the opportunity to teach students and participate in training programmes. International cooperation – Wrocław University of Science and
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image analysis packages such as Freesurfer, FSL, SPM, or 3DSlicer, or using machine learning or artificial intelligence models would be advantageous What We Offer The appointee would be exposed to ample
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employees and conducts research and teaching mainly in electrical engineering and computer technology. We are located on LTH's campus in northern Lund. Within the Division of Electromagnetics and
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Background in machine learning or deep learning methods, including Graph Neural Network (GNN) Experience with Large Language Models (LLMs) applied to biological data collection, extraction, and standardization