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• Position: TV-L E13 (65%), with the opportunity to pursue a doctoral degree (PhD) • Start date: from July 2026 • Research community: The position is embedded in the BioSysteM Cluster of Excellence, offering
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) Positions PhD Positions Application Deadline 22 Apr 2026 - 23:59 (Europe/Madrid) Country Spain Type of Contract Temporary Job Status Full-time Hours Per Week 37.5 Is the job funded through the EU Research
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is looking for an aspiring PhD candidate to research causal machine learning and uncertainty quantification for Earth Observation time-series. Currently, predictive AI in Earth Sciences relies heavily
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PhD Scholarship This PhD explores how cultural factors shape coaching practices and sporting experiences for participants. It examines ways to strengthen cultural capability across the sports sector
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of the position is three years. About the positions The lab is looking for candidates for the following two stipends: Stipend 1: Secure Embedded AI for Cyber-Physical Energy Systems (Copenhagen) The PhD position
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to the PhD programme. PhD students at the department are embedded in the interdisciplinary context of the School of Social Sciences and Philosophy which also includes economics, political science, and
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HBIGS Heidelberg Biosciences International Graduate School | Mannheim, Baden W rttemberg | Germany | 3 days ago
your application exclusively through the HBIGS application system: https://www.hbigs.uni-heidelberg.de/en/your-application Additional comments Given the interdisciplinary nature of the project, the PhD
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AI for Cyber-Physical Energy Systems (Copenhagen) The PhD position focuses on the development of secure and trustworthy AI for resource-constrained embedded systems used in power electronics and energy
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through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description This PhD proposal is part of
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records that can systematically inform preparedness, training and future response. As a result, learning from past events is fragmented, inconsistently captured, and insufficiently embedded into emergency