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Postdoctoral Fellow with Professor Morgane Austern. Professor Austern’s group focuses on research in high-dimensional statistics, probability theory, machine learning theory, graph data, Stein method, ergodic
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of location, qualitative dimensions, modal logics, graph theory, weighted networks, topology, mathematical morphology, and more. In sum: there are many ways to be one. The project goals include: pioneering
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hand and body configurations, surfaces, graphs and in permutations of inputs. Prominent architectures - including Graph Neural Networks (GNNs) and Convolutional Neural Networks (CNNs) - can be viewed as
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Theory (DFT) calculations using established codes (e.g., VASP, FHI-aims). Demonstrated experience with traditional methods for modeling atomic site disorder, such as special quasi-random structures (SQS
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denotational semantics, abstract machines, as well as string diagrams and graph rewriting. Some knowledge of category theory would be useful but not essential. Being able to formalise the frameworks and
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nonstationarity, graph theory, machine learning and multivariate statistics; with applications in neuroscience, climate research, economics and general communication networks. More information about the group
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methodological innovations that bridge the gap between computational theory and impactful clinical application. We are seeking a highly motivated individual with a strong statistical and machine learning
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Functional Theory (DFT), machine-learned force fields (MLFF), graph neural networks (GNNs), or large language models (LLMs). Extensive Knowledge In: • First-principles atomistic simulations with packages
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, group theory, and/or graph theory will be necessary. Experience in modelling biological processes, and in algorithm development or computation will also be valuable. Proven commitment to proactively