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and AI methods for materials discovery. Design and implement active-learning workflows for autonomous exploration of materials spaces. Develop and evaluate generative AI models for inverse materials
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time, with the aim of developing models, tools and competitive technological solutions to support the transition in sectors such as energy, transport and heavy industry. Within a scientifically excellent
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postdoctoral position focused on innovative research in coupled transport-energy modeling and optimization. We are looking for a postdoc to join our team. Become part of our innovative group and contribute
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expertise in network resilience or survivability and in network optimization. Solid programming skills (for example in Python) and hands on experience with mathematical optimization and network modelling. A
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If you enjoy turning complex dynamic systems into clear models and want your research to actually change how trucks can be developed into more sustainable actors in the transport system
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within the poject. You will be expected to develop a modelling and optimisation framework using open-source models for: Thermal management system modelling for novel cooling concepts. Aero-thermodynamic
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the aim of developing models, tools and competitive technological solutions to support the transition in sectors such as energy, transport and heavy industry. Within a scientifically excellent environment
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languages (for example, for hardware design or parallel programming) Array or tensor programming Applications to physical sciences, dimensional analysis, or climate impact modeling It is highly meritorious
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learning research. What you will do Propose, develop, and evaluate advanced machine learning models, including reinforcement learning methods, for the energy-aware coordination of EV fleets. Publish high
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science with strong connections to engineering, natural sciences, and industry. About the research project The PRONTO project aims at modeling public transport networks with stochastic partial differential