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of advances in the field. This new version will not be backwards compatible with v1, to allow for unconstrained optimal design. None of the above includes modelling of longitudinal data. The timepoint2 datasets
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and power (CHP), and application-specific energy storage systems. We are also exploring how quantum computing can unlock new optimization and control strategies in future power systems. Our projects
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experimental workflows including closed-loop thin-film optimization Apply AI and Machine Learning for data analysis and modelling Develop, improve and implement HW/SW concepts and components to automate
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of studying and optimizing the microstructural and mechanical properties of granular materials bonded by a solidified foam, within the framework of the ANR project BONDINGFOAM. This mission is structured around
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high-performance computing (HPC) environments Experience in computational fluid dynamics (CFD) codes and modeling Ability to present and publish results in peer-reviewed journal articles Preferred
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statistical and machine learning, deep learning, chemometrics, multimodal data fusion, computer vision, uncertainty-aware modeling, stochastic control, optimization, and deployable edge-to-cloud decision
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, processing optimization, and experimental troubleshooting Materials characterization using techniques such as GIWAXS, Raman, FTIR, UV-vis, and electrical transport measurements Understanding structure-property
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multiple ship classes and shore power applications. The project seeks to deliver industry-relevant modelling toolkits that enable optimal design and operation of greener vessels, backed by real-world
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expertise, preparing them optimally for future challenges.
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Model, working closely with the experimental team to understand this device’s PMI physics and optimize performance of the device in its upcoming campaigns. The position resides in the Power Exhaust and