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first experiments of the future quantum-computer technology that is orders of magnitude more efficient than existing quantum processors. Join us in shaping the future! As a result of five ERC grants
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of genome organisation and metabolic control—with the bold vision of building synthetic life. In this role, you will develop and apply computational methods to analyse single-cell modalities, focusing on gene
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molecules in chemistry, pharmaceuticals, and agrochemicals. The position provides a unique opportunity to work at the forefront of computational chemistry and AI, contributing to high-impact research with
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Max Planck Institute for Brain Research, Frankfurt am Main | Frankfurt am Main, Hessen | Germany | 7 days ago
circuitry of the vertebrate brain gives rise to the computational algorithms that animals use to solve real-world behavioral problems. We study this question in the larval zebrafish, where we can combine
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Science, Computer Engineering, Informatics, Data Science, Computational Social Science, or adjacent disciplines with research experience in agent-based modelling, social data science, or social network analysis and
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, structural sensing and health monitoring, conducting physical experiments, and validation of computational models. Required Qualifications: A successful applicant must have a PhD in Civil Engineering
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networks. This position is part of a UK-Canada Quantum for Science collaborative project "Quantum network applications in theory and practice" funded by STFC/EPSRC (UK) and NSERC (Canada), led by Professor
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Full-time: 35 hours per week Fixed-term: 31st March 2026 The School of Informatics at the University of Edinburgh invites applications for 2 Post-doctoral Researcher positions in Quantum Machine
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Polytechnic University aspires to leave its mark nationally, continentally, and globally. The School of Computer and Communication Sciences at Mohammed VI Polytechnic University (UM6P), Benguerir, Morocco is
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systems (ITS). In particular, the successful candidate will conduct cutting-edge research in: Developing physics-informed neural networks (PINNs) for complex dynamical systems modeling and observer design