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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 15 hours ago
Kaiserslautern and Rennes to collaborate with the project partners. Missions: 1) Study the state of the art in algorithms for memory placement in cache 2) study algorithms for the static analysis of program
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Deadline 15 Nov 2026 - 23:59 (Europe/Zurich) Country Switzerland Type of Contract Temporary Job Status Full-time Offer Starting Date 1 Apr 2027 Is the job funded through the EU Research Framework Programme
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We are a leading international university where scientific curiosity meets a hands-on mindset. We work in an open and collaborative way with high-tech industries to tackle complex societal challenges. Our responsible and respectful approach ensures impact — today and in the future. TU/e is home...
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you an engineering scientist who wishes
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of Mechanics of Materials (MoM) (www.tue.nl/mechmat) at the department of Mechanical Engineering of Eindhoven University of Technology (TU/e) launched a PhD excellence programme for sustainable materials in 2026
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(Europe/Berlin) Country Germany Type of Contract Temporary Job Status Not Applicable Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff
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will develop nanoscale devices which use magnons (collective excitations in magnetic order) as information carriers for ultra-efficient, compact, brain-inspired computing. This project is positioned
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related to multiparty computation in quantum networks. The stipend is open for appointment from 1st November 2026 or soon hereafter. The duration of the position is three years. Your work tasks The
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to evaluate and advance machine learning algorithms for one of the following application areas: “Characterising forests variations in relation to distance from pre-Columbian earthworks in the Amazon forest
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foundation models and world models enable robots that understand instructions, interpret scenes and generate behaviours. However, most systems require huge datasets, extensive teleoperation, expensive compute