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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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qualifications with a university degree (Master or equivalent) in physics, engineering or computer science Proficiency in computer vision and machine learning with Python Knowledge of X-ray physics and laboratory
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the Rheintal project Integrate CCUS and industrial processes into the energy system models Assess the energy system towards different metrics and optimization objectives such as cost and CO2 emissions Coordinate
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and in computer architecture domains, our ability to analyze data still falls behind the unstoppable data collection rates. Data-intensive applications are increasingly more demanding in sophisticated
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formulations with PFAS-free binders, in line with the sustainability goals of the project. You will explore compositions and pellet morphologies to optimize mass transport and reactivity with the mediator
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works on increasing the efficiency of industrial systems in real-world deployment by enabling process monitoring, learning, and policy training while ensuring safety and efficiency. For this, we develop
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Boundaries. However, the current process design paradigm is too slow, labor-intensive, and fragmented between disciplines. This project will investigate optimal pathways for creating a circular industry that
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, or Mixed Reality to support spatial understanding, coordination, and decision-making in a given phase or AEC discipline. Seamless Digital Processes: Integrated information management from office to site
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interventions with a patient’s clinical state, lesion characteristics, and multimodal biomarkers to optimize therapeutic effectiveness, across the pathway of care. This research axis is embedded in the CEREBRIS
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of Switzerland programme. • Develop, maintain and optimize genomic data processing pipelines and analytical workflows. • Ensure the secure ingestion, storage, organization, traceability and delivery