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Center for Devices and Radiological Health (CDRH) | Southern Md Facility, Maryland | United States | 6 days ago
Program conducts regulatory science research to ensure safe and effective interoperable medical devices, including wearable technology and systems of devices in the home and integrated clinical environments
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thick and strongly scattering samples. Experience in high-performance computing, parallel programming, or GPU-accelerated computation for large-scale 3D reconstruction. Experience applying machine
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GPU acceleration, cloud computing, and distributed architectures, to enable efficient analysis of large-scale video datasets. Collaborate with clinical and academic collaborators, external partners
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modern high performance computation facilities and parallel computing clusters (CPU and GPU). Excellent publication record and demonstrated conference presentation skills. Demonstrated ability to operate
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multidisciplinary research programme focused on developing advanced AI-driven methods for neuroimaging, digital phenotyping, and population brain health. This programme brings together expertise in artificial
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Starting Date 1 May 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer
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-erklæringen var sist oppdatert 23.02.2026 Hva er en cookie? En cookie er en liten datafil som lagres på datamaskinen, nettbrettet eller mobiltelefonen din. En cookie er ikke et program som kan inneholde
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-erklæringen var sist oppdatert 23.02.2026 Hva er en cookie? En cookie er en liten datafil som lagres på datamaskinen, nettbrettet eller mobiltelefonen din. En cookie er ikke et program som kan inneholde
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funded through the EU Research Framework Programme? Not funded by a EU programme Reference Number DR/006/26 Is the Job related to staff position within a Research Infrastructure? No Offer Description
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of the ROBERTA research project with the aim of: to explore the potential of GPU programming for treatment planning using randomized optimization approaches and the development of optimization models and