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thin films for emerging technologies. By combining state of the art physical vapor deposition, high throughput experimentation and data science, we develop thin films with enhanced performance and
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in underground facilities. The project aims to evaluate sensor technologies, design and optimize multi-sensor monitoring networks, and develop advanced detection and localization algorithms adapted
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plus. You enjoy working with complex, multimodal datasets and developing robust algorithms for continuous monitoring and predictive modelling. You are comfortable combining coding, data analysis, and
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collaboration with the Intelligent Maintenance and Operations Systems (IMOS) Laboratory at EPFL (Prof. Olga Fink). IMOS focuses on the development of intelligent algorithms designed to improve the performance
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flow reconstruction, enabling both real-time coarse diagnostics and high-fidelity offline velocity field estimation. Developing reinforcement learning (RL) algorithms for a multi-agent robotics system
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and their response to major climatic variations during and after the last ice age. The successful candidates will develop high-resolution palaeoecological records of aquatic invertebrate assemblage
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to develop original research agenda within this framework, including topics such as sectoral emissions trajectories, the effectiveness of building-related policies, and the role of financial conditions in
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phenotyping research focuses on the development of methods and models to determine traits of relevance for crop breeding and variety testing. The Agroscope research group Extension Arable Crops (Competence
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project “Responsible AI for the Swiss Judiciary” at ETH Zurich. The project develops and evaluates AI-based prototypes to support judicial decision-making, with a focus on real-world applicability. It
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the last ice age. The successful candidates will develop high-resolution palaeoecological records of aquatic invertebrate assemblage change, particularly focusing on the remains of chironomid (non-biting