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astrophysics, and uses a variety of approaches, such as stochastic processes, kinetic theory, variational analysis, finite element methods, and data-driven techniques. The Vienna School of Mathematics doctoral
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Postdoc Research Group Schur
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prior exposure to modern developments in biomathematics and will also have a solid knowledge of mathematical analysis, partial differential equations as well as kinetic theory and will be able to take
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Postdoc Research Group Chatterjee
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the network architecture need to be to capture the solution accurately? In essence, we’re exploring the frontier between modern machine learning and classical mathematical theory—where neural networks meet some
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administration The research should focus on data mining, e.g., clustering, representation learning, causality detection and graph mining. This is part of your personality: Master in Computer Science, Applied
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with graph learning, in particular using graph neural networks (evidenced by publications in top-tier journals or conference proceedings). Excellent and up-to-date knowledge of typical machine learning
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flows such as entropy dissipation. This is a chance to tackle cutting-edge mathematical and computational problems with real-world relevance, using modern approximation theory and machine learning
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group “Foundations of Cryptography” within the research group “Theory and Applications of Algorithms” at the department of Computer Science focuses on provable security of cryptographic schemes. We
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or Diploma in physics. You have experience in academic writing. You should come with a background in general quantum theory and mathematics. Ideally, you should have experience with the usual tools of quantum