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: This position is part of an extramurally well-funded research program and offers excellent opportunities to contribute to high-impact publications and to develop an independent research trajectory, including
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evaluation of machine learning, computer vision, and other algorithms, primarily in the context of health. They will be part of the thriving research community of Duke Spark (spark.duke.edu) where AI
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of ORNL researchers with various backgrounds (e.g., material science, neutron science, chemistry, manufacturing, computing, others). Interact and collaborate with researchers from universities, national
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to explore innovative ideas and develop your own research vision. Continuous support to apply for your own fellowships and excel in your future career progression. Exposure to both computational and
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the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Koç University was founded in 1993 by the Vehbi
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to unique questions with a clear vision of how to execute the project. Qualitative empirical research study involving original data collection. Preference is given for ethnographic studies, but interview
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, optimization, and characterization integrating imaging, experimental metadata, and diffraction outcomes. Design and deploy computer vision methods to detect and track crystal growth. Develop closed-loop
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Trustworthy AI Systems NSF Research Traineeship, the Digital Trade and Data Governance Hub, the Center for Machine Learning, and the Human-Computer Interaction Lab at UMD, and the Center for Equitable AI and
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methodologies to support interdisciplinary cancer research programs, including magnetic resonance imaging (MRI), optical imaging, micro-computed tomography (micro-CT), photoacoustic imaging, and image analysis
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 2 months ago
computer vision and/or audio(-visual) machine learning (e.g., multi-object tracking, speaker/source localisation, multimodal fusion). Experience with deep learning frameworks (PyTorch or equivalent) and