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fundamental research in physics-informed and symmetry-aware machine learning for nonadiabatic excited-state molecular dynamics. Develop and evaluate equivariant graph neural networks and related architectures
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Associate Research Scientist / Post-Doctoral Associate in the Division of Science (Computer Science)
discovery, machine learning, and data science. The position will provide the opportunity to develop applied research skills in machine learning, interact with an international network of collaborators, and
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biology and genomics. The position offers excellent opportunities for scientific publishing, international networking and academic career development. Our group studies the fundamental principles
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. With the rapid expansion of the observational network, encompassing over 100 stations reporting one-minute data and an additional 200 stations expected soon, traditional human-led QC processes
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potential impact across quantum networking, communications, and computing. Research Focus Design and fabricate superconducting devices that leverage the nonlinear kinetic inductance of thin superconducting
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We are seeking a postdoctoral researcher to support the project "Social networking in the era of artificially intelligent agents” funded by the Research Council of Finland. The direction is to
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machine learning, interact with an international network of collaborators, and gain post-doctoral research experience. The ideal candidate is self-motivated and can work independently, has a passion for AI
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relocation. In addition, Coming to Delft Service organises events to help you settle in the Netherlands, and expand your (social) network in Delft. A Dual Career Programme is available, to support your
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one of the following areas: - Methodology development in wavefunction-based electronic structure methods, quantum Monte Carlo, tensor networks, or quantum embedding methods
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Max Planck Institute for Human Cognitive and Brain Sciences (MPI CBS) | Leipzig, Sachsen | Germany | 3 months ago
networks, and computational modelling. Within the Department, we maintain strong synergies with the Neural Computation Group (PI: Andrej Bicanski ), which develops mechanistic computational models of brain