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of rainfall, drainage capacity, and 3D urban form on flood severity • Validate results with hydrodynamic simulations and 3D urban semantic models, benchmark against state-of-the-art methods, and publish in
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-cell data has its own statistical and computational challenges, and standard tools often cannot be applied. The purpose of the position and goal of the project is to develop and apply bioinformatic tools
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for safety-critical bilateral teleoperation. The research will leverage a combination of passivity-based control methods and machine learning techniques to enable reliable and robust teleoperation in uncertain
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(WNVI) framework and aims to advance its capabilities in the following directions: • Scalability: Extending methods to high-dimensional and three-dimensional elastography problems using neural operator
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methods in AI and machine learning, contributing directly to state-of-the-art research with high industrial relevance. Your Qualifications A strong background and Master's degree in Computer Science, AI
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/d) in Energy Informatics, specifically for a DFG project in wind power forecasting using machine learning. You are passionate about applying cutting-edge information technology to solve the energy and
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processing and machine learning methods, and big data analytics solutions to extract highly accurate large-scale geo-information from big Earth observation data. Our team aims at tackling societal grand
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Qualifications • You have a degree (Master’s or equivalent) in Civil Engineering or Mechanical Engineering with a strong focus on continuum-solid mechanics and computational methods. • You have experience
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PhD Position in Theoretical Algorithms or Graph and Network Visualization - Promotionsstelle (m/w/d)
evaluation methods Interactive systems for exploring complex networks Candidates interested in theoretical computer science will have the opportunity to work on topics such as graph algorithms, computational
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, international political economy and European integration theories. Methodologically, it draws on a plurality of approaches, notably qualitative methods.