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: MSc in materials science engineering. Backgrounds in chemistry, physics, computer science or a related area are also welcome. Good expertise or strong interest in numerical modeling, machine learning
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(‘fit for 55’) by 2030 as agreed in the European Green Deal and comply with the FuelEU Maritime and EU Emissions Trading System regulations, presenting unprecedented challenges. Up to 80% of the emissions
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an exciting opportunity to join a leading consortium at the cutting edge of satellite remote sensing, high-resolution plume modelling (MicroHH), and atmospheric chemistry transport models (LOTOS-EUROS, ECHAM
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must achieve net zero emission by 2050 and 55% emission reduction (‘fit for 55’) by 2030 as agreed in the European Green Deal and comply with the FuelEU Maritime and EU Emissions Trading System
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structural assemblies to nanomechanical resonators. In the second direction, you will explore the geometric design of nonlinear systems. Using nonlinear reduced order modelling (ROM) integrated with
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-of-the-art models in marketing science, economics, psychology, and computer science literatures relevant to understanding the phenomena. Identifying the fundamental variables, trade-offs and relationships
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the second direction, you will explore the geometric design of nonlinear systems. Using nonlinear reduced order modelling (ROM) integrated with optimization algorithms, you will design structures
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to establish a roadmap, (2) developing models and benchmarks for LLM-based refactoring, (3) designing autonomous agents, and (4) conducting studies to analyse real-world impact. We are committed to creating a
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reasoning to integrate data and theory. Methods include regression analysis, experiments, Bayesian modeling, and abductive reasoning. Our work in this area also covers how firms learn and protect knowledge
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mobility systems. In our 12 collaborative labs we apply advanced technologies such as sensing, data analytics, modelling, and AI to turn scientific research into real-world impact. You’ll join an open