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are essential, particularly in one or more of the following areas: Probabilistic or Bayesian Machine Learning Variational Inference, Ensemble, or Diffusion Models Spatio-Temporal or Sequential Modelling Graph
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Job Description The Centre for Formal Methods and Future Computing (FORM) at the Department of Mathematics and Computer Science, University of Southern Denmark (SDU, Campus Odense or Vejle) invites
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and categorical quantum theory. The Department of Mathematics and Computer Science is an inclusive and international working environment with state-of-the-art facilities and strong traditions
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create multi-fidelity predictive models that integrate data from quantum simulations and experiments, using techniques such as equivariant graph neural networks with tensor embeddings. We aim to train
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science, computational chemistry / biochemistry, applied mathematics, or a related area. The ideal candidate has strong familiarity with several of the following subjects: algorithmics, mathematical modelling, graph
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ideal candidate has strong familiarity with several of the following subjects: algorithmics, mathematical modelling, graph transformation, algorithm engineering. Applications of these areas to systems
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independently and as part of a team Preferred Qualifications Experience in graph-based AI models, multi-omics data integration, or network inference Background in epigenomics, gene regulation, or aging biology
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physics and condensed matter theories to address the problem of fracture in complex materials. You will be working with experimental model systems and numerical simulations of materials that exhibit
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practice as well as in theory. Your primary responsibilities will include: Investigate, develop and validate new models to simulate wind turbine response in HAWC2 at different fidelity levels. Validation
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physics and condensed matter theories to address the problem of fracture in complex materials. You will be working with experimental model systems and numerical simulations of materials that exhibit