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Fritz Haber Institute of the Max Planck Society, Berlin | Berlin, Berlin | Germany | about 5 hours ago
or Postdoctoral position (m/f/d) - Interpretable Machine Learning for Catalytic Reaction Network Discovery. A full-time PhD or Postdoctoral position is available in a collaborative Max Planck research
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PhD/Postdoc position in trustworthy data-driven control and networked AI for rehabilitation robotics
of future communication networks”. TU Dresden and the Technical University of Munich have joined forces to form the 6G-life research hub in order to drive cutting-edge research for future 6G communication
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, predict, and treat diseases. You will work with multimodal biomedical datasets including omics, imaging, and patient data and apply cutting-edge AI models such as graph neural networks, transformer
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-Quantencomputern und Rydberg Atom-Arrays • Numerische Methoden mit Tensornetzwerken • Topologische Ordnung und (de)confinement in string-net Modellen • Quantenfeldtheorie, Gitter-QCD Bitte senden Sie Ihre Bewerbung
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through soft, disordered materials, including auto-regulated networks, composite soft solids, and exotic photonic biomaterials. The lab has two fully funded PhD and/or postdoctoral positions available
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. German language is not required. The successful candidate will have the opportunity to work on an exciting and diverse project within a nationwide research network, with numerous opportunities for development
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and diverse project within a nationwide research network with numerous opportunities for development, working within a dynamic and international team of scientists from various disciplines, as
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and strengthen their skills to supervise (future) doctoral students and to establish professional networks through contact with other Pre-Doc awardees both awardees to participate in a wide framework
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: - QUANTITATIVE VERIFICATION: analysis of probabilistic systems (Markov decision processes, stochastic games, chemical reaction networks), automata theory and temporal logic, machine learning in verification
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pages or friendships between users in a social network. Due to the large variety in data science tasks performed with graph-structured data, different specialized systems have been developed, such as