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internationally funded projects, such as various EU Horizon projects (e.g., Smart4Welfare, URSOILL) or nationally funded projects. He/she will receive high quality supervision from the chair’s senior academic staff
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Quantum Systems (PhoQS), experts from the fields of physics, computer science, mathematics, electrical engineering and information technology conduct joint research on integrated photonic quantum
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that are technically well-grounded and at the same time represent stakeholder preferences. The integrated Research Training Group (RTG) will provide doctoral researchers with an attractive qualification program, foster
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Application Deadline 23 Nov 2025 - 11:00 (UTC) Type of Contract To be defined Job Status Other Offer Starting Date 1 Feb 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU
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description The BM2 Lab is an interdisciplinary research group at the interface of physics, computer science & biology interested in understanding morphogenesis, bio-mechanics and their interaction in living
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is jointly funded by the Faculty of Social Sciences/HKU Research Hub of Population Studies, and the Department of Digital and Computational Demography at MPIDR. The University of Hong Kong is widely
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. The fellowships are funded by the state of Lower Saxony and are part of a joint doctoral program with the state of Baden-Württemberg on the evolution of cultural modernity. Requirements for filling the positions
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funded through the EU Research Framework Programme? Not funded by a EU programme Reference Number DSS-23 Is the Job related to staff position within a Research Infrastructure? No Offer Description GESIS
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Application Deadline 26 Nov 2025 - 11:00 (UTC) Type of Contract To be defined Job Status Other Offer Starting Date 1 Feb 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU
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-edge Machine Learning applications on the Exascale computer JUPITER. Your work will include: Developing, implementing, and refining ML techniques suited for the largest scale Parallelizing model training