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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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the job funded 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 The Post-Doc
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inversion methods (LUT and hybrid approaches) Profound knowledge in machine learning and deep learning methods for remote sensing applications, including architectures such as CNNs, LSTMs, and Transformers
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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