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a circular and low-carbon economy. The PhD is expected to cover: Developing dynamic material flow analysis (dMFA) models to quantify steel stocks, flows, scrap generation, and scrap quality across
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optimal ways to schematize and parameterize the subsurface at local to regional scales to assess dike safety using geological and geohydrological data and models, for characteristic fluvial landscapes in
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employment conditions through our Terms of Employment Options Model. In this way, we encourage you to keep investing in your personal and professional development. For more information, please visit Working
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learning, Efficient Algorithms and High Performance Computing), and Physics (Image Formation Modelling). Your project is part of the DUAL-IMPACT project, which focuses on the development of new data-driven
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will then develop XR applications that provide real-time guidance through visual, auditory, or haptic feedback in everyday situations. Using wearable devices, machine learning, and cognitive models you
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will then develop XR applications that provide real-time guidance through visual, auditory, or haptic feedback in everyday situations. Using wearable devices, machine learning, and cognitive models you
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), Computer Science (Machine learning, Efficient Algorithms and High Performance Computing), and Physics (Image Formation Modelling). Your project is part of the DUAL-IMPACT project, which focuses on the development
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the muon content of air showers, confronting high-energy hadronic-interaction models and helping to solve the long-standing “muon puzzle”. You will carry out your research in the KM3NeT group at Nikhef
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seek a candidate who is excited by rigorous statistical mechanics and data-driven modelling, and who is eager to use these approaches to understand how complex biological functions emerge from
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modelling results into long-term biodiversity impact indicators and characterization factors for offshore wind farms over their operational lifetime. Developing integrated cumulative impact and scenario