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Chemistry o Combinatorics, Algorithm, Extremal Graph Theory, Computing Theory o Programming Language, AI Theory or Machine Learning o Classical and Quantum Algorithm for Computational Quantum Many-body Theory
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graph theory, for example with graph-based methods would be considered an asset. Experience with data collection and hardware setup; prior hands-on hardware development would be considered an asset
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Graph Theory, Computing Theory o Programming Language, AI Theory or Machine Learning o Classical and Quantum Algorithm for Computational Quantum Many-body Theory o Theory and Computation of Correlated
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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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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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that uncover spatial organisation and predict dynamic behaviours in complex tissue systems. Drawing on ideas from ecology and network theory, you will build new tools to model spatial biological processes
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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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, control theory, data science, data driven methods, discrete mathematics, graph algorithms, high-performance computing, integral equations and nonlocal models, linear and multilinear algebra, machine