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in screening technologies, assay development, complex cell systems and lab automation. Our projects range from biochemical screens to combinatorial chemical synthesis to sophisticated screening assays
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to developing robust, real-time models for detecting and preventing impaired driving. Your work will be key in integrating novel in-vehicle sensing approaches and AI-driven interventions into next-generation
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general area of control & dynamical systems with application to mobility. Project background The candidate will work on the “Efficient Safe Train Dynamics” (ESTRA) project, in collaboration with Prof
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, particularly on measurements and searches using jet substructure and development of advanced techniques in particle tagging, including applications using machine learning, and are expected to take leading roles
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part of ETH Zurich, which not only supports your professional development, but also actively contributes to positive change in society We are actively committed to a sustainable and climate-neutral
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forging connections all over the world, we work together to develop solutions for the global challenges of today and tomorrow.
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. The acquired and already existing database will be used to further develop ML models for the automated detection of clinically relevant markers. The goal is to develop the technique towards potential clinical
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) initiative for Developmental and Reproductive Toxicity (DART) testing. This project aims to further develop a research community that is currently establishing a regulatory roadmap for DART testing using
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is to clean up, organize, and prepare the diverse datasets collected during the Cloudlab seeding experiments. This involves working with data from a suite of in-situ and remote sensing instruments
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on the development of state-of-the-art X-ray optics through the latest micro- and nanofabrication techniques. We are seeking a highly motivated PhD student to join our SNF-funded project, ABSOLUTE, under the guidance