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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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perturbation modelling. The ideal applicant brings not only strong technical skills, but also interdisciplinary knowledge on the subject. More precisely: PhD degree in computer science, machine learning
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or simulated industrial scenarios. Is Your profile described below? Are you our future colleague? Apply now! Education PhD in Computer Science, Telecommunications, or a closely related field. Strong background
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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