117 machine-learning "https:" "https:" "https:" "https:" "https:" "UCL" positions at University of Southern Denmark
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following thematic areas: • AREA 1: Machine learning and AI-driven methods for design, simulation, and optimisation in architectural and construction engineering. • AREA 2: Robotic and additive
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sound, with human speech at the pinnacle of complexity. Like human babies, songbirds learn their vocalizations early in life from a social tutor. Numerous parallels to human speech learning, including
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-motivation to learn. Have the ability of working independently. Have a strong sense of responsibility. Research environment The candidates will be integrated in the unit SDU Robotics, part of the Maersk Mc
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research infrastructure A competitive salary and social benefits (e.g., health coverage, parental leave, social security, etc.) (https://www.sdu.dk/en/om-sdu/international-staff/getting-settled ) Workplace
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strategy (PMIDs: 29123070, 33621493, 33087936, 30566856, 39947938; doi: https://doi.org/10.1101/2025.03.15.641049 ). Postdoctoral Projects Project 1: Replisome Dynamics, Replication Stress, and Cancer
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-of-the-art equipment, facilities, and infrastructure. • A competitive salary and social benefits (e.g., health coverage, parental leave, social security, etc.) (https://www.sdu.dk/en/om-sdu/international-staff
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more about the CAPeX research themes and X-trails, our organization, and the other open PhD and postdoc cohort positions at http://www.capex-p2x.com . We look forward to receiving your application and to
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, parental leave, social security, etc.) (https://www.sdu.dk/en/om-sdu/international-staff/getting-settled ) Workplace description POLIMA is located at the University of Southern Denmark in Odense – 1h15min by
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opportunities to explore the intersections between these disciplines. Reda more here: https://www.sdu.dk/en/om-sdu/institutter-centre/fysik_kemi_og_farmaci/ominstituttet Job Info Job Identification 3512 Job
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computer science. The candidate is expected to have solid knowledge in most of the following areas: Robotics Control theory Deep Learning & Machine learning Modelling and control of soft/continuum robots Experience