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Field
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understanding of data quality, reproducibility and robust analytical practice. Experience of SQL, cloud-based or high-performance computing environments, and Bayesian methods would also be valuable. Beyond
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networks, Bayesian inference, computational neuroscience, mathematics.
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diverse set of time-dependent forecasting models (e.g., neural network, mechanistic, statistical, and data-driven) to serve as experts within the integrative architecture. (iii) Mixture-of-experts
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projects such that the successful candidate will have excellent opportunities to participate in international research networks. We also run an advanced drone lab on behalf of the entire faculty
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across AI and both experimental and computational chemistry. This Hub will promote connectivity of the broader community, training, and networking, as well as funding state-of-the-art research. Based in
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experimental and computational chemistry. This Hub will promote connectivity of the broader community, training, and networking, as well as funding state-of-the-art research. Based in the Cooper Group
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quantum magnetism and strongly correlated systems, as well as classical methods such as exact diagonalization, tensor networks or DMRG, and quantum Monte Carlo. Familiarity with inelastic neutron scattering
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via nonlinear parametrizations such as deep networks, dynamical systems and control, Bayesian inference and generative modeling, and randomized linear algebra. Applications of interest are transport
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, recurrent memory, Bayesian modelling, uncertainty quantification and machine learning systems. Emphasis will be on methods that design and implement new architectures for (auto-regressive) sequence modelling
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that leverage state-of-the-art AI methods (deep learning, generative AI, Bayesian modelling, active learning, etc.) to combine cellular imaging data, chemical compound structure, viral genomes and other omics