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, including researchers, faculty members, engineers and technicians, as well as non-permanent staff, including fixed-term contract researchers, postdoctoral researchers, PhD students and interns, distributed
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Date 07/22/2026 Requisition Number PRN45696B Job Title Post Doc Res Assoc Working Title Postdoctoral Researcher for SLA/DLP 3D Printing Career Progression Track A00 Track Level FLSA Code Administrative
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, computational physics, or a related field; • Strong background in machine learning, probability, and statistics; • Proficiency in scientific programming with Python and deep-learning frameworks such as PyTorch
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. • Karimi H. et al., “Wavelet Based Protection of Microgrids”, IEEE Transactions on Smart Grid, 2019. • Heidari A. et al., “Deep Learning for Fault Detection in Smart Grids”, Applied Energy, 2021. • Wen L. et
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of the four campuses (two in Chicago, one in London, and one in Hong Kong) reflects the architectural traditions of its environs while offering a state-of-the-art learning environment. Chicago Booth is proud
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). Desirable assets are: • Machine learning, deep-learning, artificial intelligence, advanced statistical inference; • A solid record of research activities, including relevant publications in international peer
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for sustainable transport planning. The purpose of this PhD project is to develop modelling approaches that improve the ability to analyse future desirable transport scenarios under deep uncertainty. A central
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, Python or similar) Specific Requirements Interest in optical imaging and interferometry. Prior knowledge will be a plus Interest in computational methods, image processing and machine/deep learning. Prior
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for changes in your work tasks after appointment Where to apply Website https://www.jobbnorge.no/en/available-jobs/job/307999/phd-research-fellow-in-gr… Requirements Research FieldTechnology » Environmental
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% of your time), including tutorials and supervision of Bachelor’s theses. This is what we ask of you This is an interdisciplinary project that combines machine learning and AI, probabilistic risk