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Field
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Postdoctoral Associate to advance cutting-edge research in machine learning (ML). Our lab explores the intersection of artificial intelligence, and human-computer interaction, striving to create technologies
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programming; experience in Python for machine learning and/or digital image processing is preferred. Strong communication skills, both in formal written reports/manuscripts and oral presentations. Language
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integrated circuits (IC) and printed circuit boards (PCB). Additionally, the candidate should demonstrate expertise in applying computer vision, image analysis techniques, machine learning, deep learning to IC
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applications* in close collaboration with other discipline experts (software, microelectronics and applications engineers). * except for RF payloads. ** including artificial intelligence and machine learning
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Monitoring (LTVEM) in the hospital for management and diagnosis of epilepsy. The technology is built on brain computer interfaces equipped with a Spiking Neural Network (SNN) and aims at early detection
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model APIs, cloud computing environments, and R for additional statistical analysis. For decision support prototype development and evaluation, web-based user interface design, human-computer interaction
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 month ago
of microscopy and imaging techniques is a plus. Willingness to learn new in vivo and in vitro techniques, such as performing animal surgeries, intra-muscular, intra-articular and intravenous injections
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, research, and public service. Job Description Purpose: The Department of Electrical and Computer Engineering and AggieFab Nanofabrication Facility at Texas A&M University seeks a Postdoctoral Researcher to
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Posting Details Posting Details Posting Number 0801675 Classification Title Postdoctoral Research Associate- Autonomous Systems and Machine Learning Working Title Department College
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optimizing the squeezing of the vacuum to minimize quantum noise, a prototype cryogenic interferometer, using machine learning for nonlinear feedback control, devising techniques to quell opto-mechanical