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Field
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theory & experiment: Co‑design validation experiments with experimentalists; iterate models using feedback from new measurements. Automate the workflow: Build Python workflows for simulation and data
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& advance digital twins: Integrate electronic structure (e.g., DFT, ab initio MD, tight-binding) with multiscale simulations to predict experimental observables at interfaces. Bridge theory & experiment: Co
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-based networks graph-based approaches Bayesian learning information theory Documented strong programming skills (preferably Python), for example with contributions to open-source projects, with an active
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topics such as: neural networks self-supervised learning convolutional neural networks transformer-based networks graph-based approaches Bayesian learning information theory Documented strong programming
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theories and improve our understanding, lending itself to better solutions. Willingness to share your insights is crucial to achieving a vibrant and creative research environment, contributing to the shared
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statistical physics. Specific Requirements Preference will be given to candidates with experience in research related to machine learning, graph theory, statistical physics, and modeling of stochastic systems
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Field Theory and Mathematical Physics Group, Division
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the master's degree has been awarded. A strong interest in Algorithmic Number Theory and/or Cryptography is a requirement. Experience in programming is an advantage. Experience in developing and coding
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series, and Bayesian analysis. In applied mathematics, these include information theory, coding theory, control theory, fluid mechanics, and mathematical biology. Further details on the departmental
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will work at the interface of first-principles theory (e.g., DFT) and reactive force field modeling (e.g., ReaxFF), developing multiscale, high-throughput workflows that simulate and optimize growth