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interdisciplinary project on the development of new computational approaches for improving safety and sustainability of (nano)materials based on publicly available data and tools, including omics data, and
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work closely with another PhD student and a post-doc to collectively investigate the failure mechanisms and the generation of high-quality sensor data. Your profile MSc in Materials Science or Mechanical
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components and nucleic acids interact and self-assemble. Apply data analysis and modeling to deepen understanding of nanoparticle architectures, and contribute to standardization-relevant method development
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-PDF), data analysis and modelling for partially crystalline material systems Developing lab-based X-ray multimodalities: X-ray diffraction & imaging presentation of scientific results through
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optimization of community PV systems, e-mobility, and edge data center integration at the Swiss pilot site hosted at Empa’s NEST demonstrator. The role involves coordinating the deployment and validation
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temperature and humidity data in cold chains by commercial sensors, and deploy them in end-to-end virtual supply chains. This project also aims to better understand the tradeoffs related to cooling technology
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programming, modelling, and data analysis skills. Experience with formulating and solving mathematical optimization problems is an asset. Proficiency in English is required; good comprehension and oral skills
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understanding of district heating and cooling, renewable energy integration, multi-energy systems, and energy conversion and storage technologies. You have strong skills in programming, modelling, and data
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laser processing and to bring your ideas in AI/ML to the technology level. You have a solid background in programming (deep learning, reinforcement learning, etc.), electronics, high-speed data
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microscopes, and the unique Aurora robotic battery materials research platform equipped with more than 1500 battery cycling channels and automated workflow and data management capabilities. Empa is committed