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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 22 hours ago
professionalism, enhance learning, and create personal and professional sustainability. We optimize our partnership with the UNC Health System through close collaboration and commitment to service. OUR VISION Our
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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presentation/publication of research findings. Candidates with a Biology and Biomedical Science related Master's degree or DVM degree in addition to a PhD are preferred. This position will require attention
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We are seeking a Postdoctoral Researcher in Human-AI interaction to join a research group focused on studying learning and decision-making in humans and machine learning systems led by Prof Chris
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extension. Your Tasks Our overarching goal is to understand the neural mechanisms of cognitive functions, focusing on learning, attention and decision making. We investigate the contribution of subcortical
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timings) affect the metabolome and proteome of rapeseed seeds. Your findings will serve as molecular fingerprints to support Deep Learning models for hybrid development. Whom we are looking for: An early
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Minimum of $54,000 based on experience Type of Position Other Position Time Status Full-Time Required Education PhD degree required. Required Related Experience 5 years (including Ph.D. training) Required
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functional magnetic resonance imaging: fMRI) methods to investigate iconicity in spoken language – the idea that the sound of a word may convey its meaning. The successful candidate will have a PhD in a
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The University of Oxford is seeking a highly motivated Postdoctoral Scientist with expertise in biostatistics, machine learning, and cardiac magnetic resonance imaging (MRI) to join Professor Betty
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available single-cell sequencing data generated from patient samples and mouse models, we will enhance and apply machine-learning based algorithms to deconvolute bulk tumor RNA-seq samples to distinct immune