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Field
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the coordination of large-scale robot systems (ground and aerial). The ideal candidate will possess hands-on experience with designing and implementing reinforcement learning algorithms, and deploying them onto real
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methods for eliciting and aggregating safety specifications and risk thresh-olds for AI systems, with a particular focus on mechanisms that provide axiomatic guarantees, are algorithmically tractable, and
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space systems; Investigating and implementing PQC algorithms to safeguard space data systems against quantum computing threats; Evaluating the performance and security of PQC solutions in the context
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
Models (LLMs) and clinical data analysis. About the Position Our Postdoctoral Research Program is designed for candidates who have completed their PhD within the last two years and have experience as
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supervised by Zhandong Liu, PhD, based in the Neurological Research Institute (NRI) at Baylor and Texas Children’s Hospital. The successful candidate will focus on The NRI brings together world experts in
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. The successful candidate will work at the intersection of multi-disciplinary modelling, advanced AI algorithms, and decision-support tool development. Responsibilities will include programming, analysing and
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on transplant using multimodal medical data. You will be responsible for literature review, data cleaning, model development and implementation. You should possess a relevant PhD (or near completion) in
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physics. Our experimental responsibilities include trigger algorithms and performance, detector calibration, and jet energy corrections. The two appointed candidates will work within the research project
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. Qualifications PhD in Computer Science, Economics, Mathematics, or a closely related dis-cipline. Strong background in computational social choice, algorithmic game theory, or AI safety. Excellent research track
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, ballistocardiography, and bio-radar) in combination with machine learning based algorithms for time series analysis into the whole OSA diagnosis and treatment pathway. During diagnosis unobtrusive sensors that can be