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materials; superconductivity; collective excitations (e.g., excitons, magnons, skyrmions); integer and fractional quantum (anomalous) Hall effects; machine learning approaches for quantum many-body problems
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project focused on the development machine-learning powered digital twin system for the structural performance of civil engineering structures. The project is a collaboration between multiple research
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at one of OCP Group’s production sites. The project will rely on Operations Research and Machine Learning approaches. The objective is to redesign the extraction methods by considering their impact on
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group (https://bckrlab.org). We focus on high impact applications and work on knowledge-centric AI and biomedical machine learning including multi-omics integration, single cell analysis, and sequential
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in solid mechanics framework Experience in non-linear solid material response and fracture modeling Experience in machine-learning modeling for solid mechanics applications Experience in
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tickets and car sharing, a wide range of sports offered by the ASVZ , childcare and attractive pension benefits Working, teaching and research at ETH Zurich We value diversityIn line with our values , ETH
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following: Quantum chemistry (preferably of excited states) Multiscale simulations/environmental modelling Excited state dynamics Data Science/Machine learning in chemistry/Cheminformatics Molecular dynamics
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AgriLife is uniquely positioned to improve lives, environments and the Texas economy through education, research, extension and service. Click here to learn more about how you can be a part of AgriLife and
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physics. Participation in detector research and development, especially for low-latency event selection in trigger systems. Development of new artificial-intelligence and machine-learning techniques
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networks. The research will employ mathematical modelling and computer simulation to identify synaptic plasticity rules which enable effective learning in large and deep networks and is consistent with