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human-computer interaction, as well as participate in the full range of faculty responsibilities, including scholarly research and service to the University. Applicants should possess a PhD degree in
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scientists, statisticians, and NHS partners. You will also represent the project at local, national, and international meetings, events, and conferences. Based at King’s College London, you will be expected
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automation, programming, scripting languages such as Python, and algorithm development. You will have extensive experience of software development / PhD in Computing or in Chemistry with a strong computing
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of Machine Learning, Computer Vision, Large Language Models and related technologies. Relevant publications – articles in peer-reviewed scientific journals indexed in WOS with an impact factor. Excellent
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successful in this position, you will ideally bring the following: For the Networking and AI position: Completed a PhD in Computer Science, Electrical & Computer Engineering, or a closely related field Expert
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of the Institute is to "Optimize Combat Casualty Care". For more information about the USAISR, please visit https://usaisr.health.mil/. About ORISE This program, administered by Oak Ridge Associated Universities
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/InstituteInstitute of Computer ScienceCountryEstoniaState/ProvinceTartumaaCityTartuStreetNarva mnt 18 Contact City Tartu 51009 Website http://www.ut.ee/en Street Ülikooli 18 E-Mail jaak.vilo@ut.ee STATUS: EXPIRED X
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Engineering, or a closely related field. Required qualifications for graduate teaching include a PhD or terminal degree in Computer Engineering, Electrical Engineering, or a closely related field (preferred
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manual gestures). The SyncoGest project (2025–2030) is an interdisciplinary project conducted jointly by computer scientists (Loria – University of Lorraine / Inria / CNRS), linguists (Praxiling – Paul
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science/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning