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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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translational and scalability considerations. Responsabilities: Lead the development of hybrid foundation model–graph neural network architectures for gene perturbation prediction, including the design and
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2027 - 03:31 (UTC) Country Luxembourg Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to
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Models, Generative AI, Federated and Decentralized Learning, Neurosymbolic and Hybrid AI, Self-Supervised and Few-Shot Learning – and their integration into wireless communications and edge computing
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programmable connectivity systems. The successful candidate will conduct research at the intersection of AI and networking, contributing to advanced architectures and intelligent, sustainable connectivity
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in the light of ongoing European projects. The candidate will play a central role in developing quantum communication protocols, designing quantum network architectures, and building SDN-enabled