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-wide traffic prediction Design physically consistent and interpretable machine-learning methods for dynamic traffic systems Test and validate prediction models using large-scale real-world traffic data
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PhD candidate you will develop new ways to extract cosmic-ray physics from KM3NeT data. You will design and characterise reconstruction methods—both machine-learning-based and traditional
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in integrating open science in their research practice. The Faculty of CEG comprises 28 research groups in the following seven departments: Materials Mechanics Management & Design, Engineering
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and space to shape your work independently and develop your ideas. A close-knit community of colleagues to collaborate and grow with. A solid pension plan via ABP, company fitness schemes, and access
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we will devote attention to your onboarding. If some of the work activities are new to you, we will look together at what you need, and draw up a development plan. This position is a good fit for you
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magnetic moment and serve as the building blocks of molecular quantum devices for next-generation technologies. As the design principles for developing molecular magnetic behavior become clearer, the next
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technologies. As the design principles for long-lived molecular magnets become clearer, the next challenge is assembling these units into spin lattices via surface deposition and addressing their magnetic
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in which everyone feels welcome and appreciated. Our organisations are always evolving and we need your ideas for improvement and innovation to take us further. We want to devote attention to your
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route to produce high-quality recyclates, but current implementations rely on static process design and control that don’t account for feedstock variability, inefficient solvent use, and high energy
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, and companies across the value chain of biomass. In this PhD project, you will develop state-of-the-art methodologies to: Design and scale up novel processes for the valorization of low-grade biowaste