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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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-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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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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scientific career. About us TUM’s new Computational Pathology and Medical Machine Learning lab (*2021) develops methods of machine learning (ML) and artificial intelligence (AI) for the analysis of digital
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, international political economy and European integration theories. Methodologically, it draws on a plurality of approaches, notably qualitative methods.
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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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: Excellent Master’s degree (or equivalent) in computer science, engineering, or related disciplines (typically mathematics, physics). For Postdoc applicants: Excellent track record in computer
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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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tools? This research area sits at the intersection of Human-Computer Interaction (HCI), Artificial Intelligence (AI), and Software Engineering (SE). The goal is to design and evaluate new interaction
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novel machine learning-guided approaches. The position is located at TUM Campus Heilbronn. Your qualifications Strong background in computer science, AI, or related areas or similar fields. Solid