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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 21 days ago
machine learning applied to natural language processing, computer vision, or a related area. Strong publication record in NLP, ML, or related areas -Strong programming skills, including TensorFlow and/or
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and Liu, Supervised learning in physical networks: From machine learning to learning machines, PRX 11, 021045 (2021) [2] Stern and Murugan, Learning without neurons in physical systems, Ann Rev Cond
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Surgery Stanford Departments and Centers: Surgery, General Surgery Postdoc Appointment Term: 1 year Appointment Start Date: July 1, 2026 Group or Departmental Website: https://med.stanford.edu/gensurg
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of belonging. The Postdoctoral Associate also will participate in weekly seminars with other Active Learning Initiative postdocs, receiving training and support in designing and implementing research-based
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and simulation Prior experience with particle accelerators and/or FELs is highly desirable Familiarity with machine learning techniques is a plus but not necessary Excellent command of English is
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Are you interested in developing machine learning algorithms that provably help us make better decisions? Join us as a post-doc in the Division of Data Science and AI, Department of Computer Science
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strategies, and (ii) NIDS evaluators, to enable more informative and reliable evaluation protocols for ML-based IDS/NIDS. Where to apply Website https://institutminestelecom.recruitee.com/o/postdoc-qualite-des
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) signal processing, machine/deep-learning and computational linguistics. The team mobilizes them to produce methodologically sound research in response to some of the challenges posed by the nature and
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of working with motion capture, eye tracking, machine learning, or other advanced behavioral analyses or related research experiences. A consistently excellent academic track record is required, including
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Postdoctoral Researcher in Natural Language Processing and Digital Humanities (18 months, full-time)
Intelligence, Machine Learning, or Computational Linguistics Digital Humanities or Linguistics with a strong computational focus Classics, History, Philology, or related humanities disciplines with documented