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on a new project called TRUSTLINE, which is part of the Learning Introspective Control (LINC) DARPA Program. The project aims to develop machine learning (ML)--based introspection and monitoring
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intelligence experts to generate new projections of the land ice contribution to sea level rise until 2300 with machine learning. You will develop probabilistic machine learning “emulators” of multiple ice sheet
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experience in machine learning and image analysis for ultrasound images and video. The successful applicant will possess specialist experience conducting fieldwork, particularly in low-resource or rural
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2025. We seek to recruit a Research Associate specialising in statistical modelling and machine learning to join our multi-university multi-disciplinary team developing a groundbreaking technique based
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skills Desirable criteria Experience of atomistic modelling of ferroelectric materials Experience in development and application of machine learned potentials * Please note that this is a PhD level role
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50 Faculty of Life Sciences Startdate: 01.10.2025 | Working hours: 40 | Collective bargaining agreement: §48 VwGr. B1 lit. b (postdoc) Limited until: 17.11.2025 Reference no.: 4674 Explore and teach
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of atomistic modelling of ferroelectric materials 2. Experience in development and application of machine learned potentials * Please note that this is a PhD level role but candidates who have submitted
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climate scientists and artificial intelligence experts to generate new projections of the land ice contribution to sea level rise until 2300 with machine learning. You will develop probabilistic machine
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properties of Li-rich three-dimensional materials for lithium battery cathodes using density functional theory (DFT), molecular dynamics, cluster expansion, machine learning computational techniques. This work
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no.: 4644 Explore and teach at the University of Vienna, where over 7,500 academic minds have found a unique blend of freedom and support. Join us if you're driven by a passion for top-notch international