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engineer and Postdoc P1 to ensure cross-vector coherence in infrastructure models and boundary conditions. Participating in the design and execution of scenario simulations, including policy- and technology
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road conditions. Your specific activities will include (but are not limited to): • Develop robust, production-grade machine learning solutions for predictive modelling and complex decision
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surface properties. Many of these properties are believed to represent adaptations to specific environmental conditions, resulting in distinct distributions of certain combinations of leaf properties
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. We look for researchers from diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography
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the Energy sector. Such a platform puts together multidisciplinary data and models covering different aspects of the energy transition, including energy infrastructure, market design, regulatory constraints
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Pneumatic Tires, Structure-Process-Properties Relationships. How will you contribute? Do you have proven skills in data analysis, machine learning, as well as in mathematical and computational modelling? You
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experiments with quantitative microscopical analysis and physics-based modelling to understand how conifers solve the challenge of solute transport against the flow of water through the needle, and what
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61850, IEC 60870-5-104, IEEE C37.118. Familiarity withgrid-forming control principles, converter modelling and deployment of real-time distributed responses to improve grid conditions. Experience working
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twins together with two PhD students, especially to propose new models and algorithms for complex maneuvers, and building a parametric autonomous model of drivers reproducing a close to reality human