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simulations so expensive that fitting them demands new algorithms and scalable software. We apply this to real questions, from how tissues self-organise during embryonic development to digital twins
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100%, Zurich, fixed-term The Biomedical Image Computing Group develops algorithmic solutions to enable automatic interpretation and forming of biomedical images, aiming to increase efficiency and
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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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on developing intelligent humanoid robotic systems using next-generation, single-chip RGB-D sensing technologies. The project aims to integrate novel depth-sensing hardware with advanced perception algorithms
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to distributed execution, checkpointing, filesystem performance, networking, and GPU utilisation Help maintain reproducible training recipes, configuration files, launch scripts, and documentation Work with
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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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for a highly motivated Postdoctoral Researcher to join our group at ETH Zurich and investigate quantum algorithms for turbulent fluid dynamics. The project focuses on the Lattice Boltzmann Method (LBM
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are looking for someone who can take ownership of a prototype from concept to a complete working hardware/software system, rather than focusing exclusively on simulation or algorithm development. Teaching
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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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research in robotic design and algorithms through real-world applications such as a robotic fish for underwater locomotion , soft robotic hands for pick-and-place tasks , and multi-segment arms for dynamic