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to algorithmic innovation, approximation, and scalable computing in high-dimensional and data-rich applications. Potential application areas include engineering or physical problems, problems with distributed
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. The successful candidate will pioneer new algorithms for graph-structured data, revisit classical graph problems through the lens of modern machine learning, and help define the next generation of generative
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techniques to simplify sensor calibration, analyze measurement data and develop adaptive algorithms for intelligent sensing applications. Conduct excellent research: Publish your research in leading
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or artificial intelligence (e.g. explanation of and trust in algorithms). The aim of this professorship is to establish solid connections and collaborations between the Department of Philosophy and the Machine
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tuberculosis (TB) screening research, spanning the evaluation of novel, high-throughput molecular tests, innovative screening algorithms, and digital health tools. A core component of the role involves
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Max Planck Institute for Astronomy, Heidelberg | Heidelberg, Baden W rttemberg | Germany | about 1 month ago
coordination between real-time hardware and software and novel, machine learning-based predictive algorithms. The hardware for the METIS AO system, along with an ELT telescope simulator, has been set up in
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information is contained in these data and develop the computational and statistical approaches needed to extract it. Working closely with experts in imaging technology, algorithm development, biology, and
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. ▪ Working closely with partners at TUD and theoretical researchers on algorithm development, performance analysis, implementation, and experimental validation. To be qualified for this position, you should
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outstanding record in applied mathematics with an emphasis on optimization. A research orientation combining theory and algorithms for optimization with partial differential equations with mathematically
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via the Emmy Noether project “Stability and Solvability in Deep Learning”. This project focuses on mathematically analyzing machine learning algorithms with a particular focus on questions of stability