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Develop algorithms for reconstruction of virtual histopathology images from sparse phase-contrast CT acquisitions of oncological specimens Comparison with standard histology and assessment of algorithm
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Our research group focuses on the development of AI algorithms for industrial applications. The main scope of our activities is the optimization and automation of workflows and production systems
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100%, Zurich, fixed-term The Algorithms and Visualization Group in the Social Networks Lab at ETH Zürich is headed by Prof. Dr. Ulrik Brandes. We develop network data science methods
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, optimization and inverse problems, computational aspects of stochastic (partial) differential equations, randomized numerical algorithms, structure-aware and structure-preserving numerical methods for continuum
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develop novel learning-based control and policy optimization techniques. We're looking for a skilled machine learning (ML) engineer to develop cutting-edge AI algorithms for digital twin applications in
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Iterative Learning Control (ILC) algorithms on real-world industrial platforms. The project requires good theoretical and hands-on expertise. Perfectly suited for those at the intersection of theory and
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open questions in the field live. Research directions include: Using AI to learn parameters and design circuits for quantum optimization algorithms (e.g., beyond QUBO formulations for QAOA) Developing AI
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, technologies, protocols, and AI algorithms to the clinics and industrial partners. Therefore, experience in or a strong link to clinical medicine is desired for the position. The new professor is expected
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methods and algorithms in the context of multi-modal synthetic aperture radar (SAR) for the estimation of environmental parameter over arctic regions SAR is an active remote sensing technique for estimating
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, dexterous, capable, and adapt better to their environment. The lab has showcased its research in robotic design and algorithms through real-world applications such as a robotic fish for underwater locomotion