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methods), Gaussian Random Fields, mathematical foundations of deep learning, functional analysis and measure theory. Research competence & initiative demonstrated through international publications in peer
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interest in Applied Mathematics, possesses solid and profound background knowledge in Numerical Analysis, Approximation Theory and Machine Learning, can easily integrate into our team, independently
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working in the research group “Theory and Applications of Algorithms” at the Faculty of Computer Science. The position is limited to six months and is planned to be filled from 01.10.2025. Your future tasks
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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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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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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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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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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