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force fields (MLFFs) that combine state-of-the-art equivariant neural network architectures with robust, well-calibrated uncertainty estimates. These models will enable fully automated active learning in
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for We are seeking a motivated computational biologist with a passion for cancer genomics and a drive to tackle complex biological questions. Essential: PhD (or studying towards a PhD) in computational
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expected to take a leading role in the research projects in particle physics phenomenology particular in relation to Higgs, neutrino and BSM physics, and help in supervising PhD students. The appointment
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Employment 1.0 FTE Gross monthly salary € 4,728 - € 6,433 Required background PhD Organizational unit Faculty of Social Sciences Application deadline 08 March 2026 Apply now Join the ERC project
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
modeling of large-scale dynamics in networks. This role involves creating large scale models of dynamic phenomena in electrical power networks and quantifying the risk of rare events to mitigate
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the highly collaborative and stimulating environment of the PaquetLab, ISD, SyNergy Cluster of Excellence, and our international collaboration network. See our recent publications in Nature Neuroscience
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academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and
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probably fair to say that an artificial neural network can perform better than a human in any environment it has complete knowledge of. These developments however impose growing demand on our computing
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academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and
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highly qualified talent. We look for researchers from diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud