43 algorithm-development-"Multiple"-"Prof"-"Prof"-"SUNY" Postdoctoral positions in United States
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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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scale and resolution. This ambitious project spans multiple institutes including the Wu Tsai Neurosciences Institute, Stanford Bio-X, and the Human-Centered Artificial Intelligence Institute, bringing
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are looking for highly talented developers with experience and interest in state-of-the-art technologies, high performance computing (HPC), memory management, and dev-ops. You will enjoy being part of a world
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, modeling, and analysis for the SCHOLAR project. Develop and deploy the SCHOLAR dashboard, incorporating CHW feedback through multiple design iterations. Collaborate with CHWs and senior research personnel
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large quantities of data to gain a greater understanding of our systems and develop data analytics and artificial intelligence algorithms. You will be actively engaged in the research and development
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develop signal processing algorithms to characterize structural health in microreactors and other advanced nuclear reactor technologies. Metrics for success will include scientific output, disseminating
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Details Position Description The University of Washington, Department of Radiology, has openings for multiple postdoctoral scholar positions. The research projects will focus on advancing rapid
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of application development techniques (numerical methods, solution algorithms, programming models, and software) at scale (large processor/node counts). A record of productive and creative research as proven by
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leading peer-reviewed journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration
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. Proficient with running machine learning algorithms (e.g., Random Forest, CART) and regression models (e.g., SAR, LME) to derive ecological insights from big data sets. Experience developing reproducible and