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Field
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algorithms. We welcome applications from individuals with experience in: Experience developing deep learning models for real-time image/video segmentation, object tracking, reinforcement learning. Deep
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training algorithms and AI architecture. Image reconstruction, segmentation, and classification. High performance computing for spatiotemporal data. Major Duties/Responsibilities: Develop foundation AI
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Université Grenoble Alpes, laboratoire TIMC, équipe GMCAO | Grenoble, Rhone Alpes | France | 18 days ago
precision and treatment decisions. Within a collaborative project involving researchers, clinicians, and industry partners, the team has developed innovative deep learning–based segmentation methods and built
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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | 3 days ago
computationally intensive, relying on iterative time-marching algorithms to reconstruct heating environments. This limits their use in more advanced computational analyses, such as uncertainty quantification
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 9 hours ago
. * Developing computational and algorithmic approaches to understanding the neurobiological mechanism of neurodisorders. * Interact cross-functionally: work with people across the team to find creative solutions
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Vacancies Postdoc position on deep learning based medical imaging for medical robot Key takeaways In this role, you will help develop and implement cutting-edge AI solutions for real-time, image
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 5 hours ago
. Description: Uncertainty quantification is a field with wide applicability to the design, development, and science return of space science missions. There is growing interest for the tools and practices
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accurate completion. Algorithm Design: Design and implement algorithms for tensor completion, considering the unique challenges posed by sparse and multidimensional network data. This involves developing
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. university groups within CMS. Our instrumentation expertise spans detector operations and upgrades (Level‑1 muon trigger, CSC muon system), tracking algorithm R&D, and future HL‑LHC trigger algorithm
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and analysis of mathematical methods for novel imaging techniques and foundations of machine learning. Within the project COMFORT (funded by BMFTR) we aim to develop new algorithms for the training