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and research in several areas. These include, but are not limited to: Adversarial location and network interdiction models Adversarial machine learning attacks and defense (e.g., against Bayesian
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salaries, an array of benefits, an extensive support network, and above all, an enriching and highly collaborative working community that is deeply passionate about our vision for higher education, research
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Qualifications Experience with graph neural networks, machine-learning interatomic potentials, or related scientific machine-learning methods for atomistic systems. Familiarity with uncertainty quantification
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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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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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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
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international 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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behavioral data, time-series data, sensor-based data, physiological signals, movement tracking, or related complex datasets. Some knowledge of Unity game engine development and experience with network
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Implementing Bayesian networks and uncertainty quantification techniques to account for sensor noise and model confidence limits Designing, training, and fine-tuning computer vision models to extract clinically