81 machine-learning "https:" "https:" "https:" "https:" "https:" "Univ" "Univ" positions
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of machine learning What We Offer: On the basis of full-time employment (40 hours/week) the minimum salary in accordance with the collective agreement is € 5,014.30 gross per month (14 x per year, CA Job Grade
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astrophysics (completed by the start date), demonstrated experience in large-scale structure simulations, working knowledge of applications of machine learning techniques in cosmology and/or astrophysics (in
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land- and sea-grant research university, located in Kingston, Rhode Island, a beautiful seaside community well connected by car, bus, and rail. URI is approximately 40 minutes from Providence, an hour
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Architecture to start in Fall 2026. URI is the State’s flagship land- and sea-grant research university, located in Kingston, Rhode Island, a beautiful seaside community well connected by car, bus, and rail. URI
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learning meet in the heart of a global city. We’re the state’s premier public urban research university with more than 100 in-demand, top-ranked bachelor’s, master’s, and doctoral degree programs. We partner
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. Skills in computational modelling or machine learning applied to brain signals are an asset. We are looking for a highly motivated, rigorous and curious researcher who is ready to invest themselves in a
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| Working hours: 30 | Collective bargaining agreement: §48 VwGr. B1 Grundstufe (praedoc) Limited until: 31.03.2030 Reference no.: 5335 Among the many good reasons to want to research and teach
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implementation of faculty development initiatives that support more than 300 instructors teaching UNIV courses, including University 101, 201, and 401. This position works closely with instructors who teach first
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comprehensive platform for data extraction, analysis, and version control, providing access to highly curated datasets in a machine learning-friendly format. This PhD is part of the CARES project (Chemically
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. 132, no. 3, pp. 1521–1534, 2012. [6] S. Koyama, J. G. C. Ribeiro, T. Nakamura, N. Ueno, and M. Pezzoli, “Physics-informed machine learning for sound field estimation: Fundamentals, state of the art, and