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Three PhD positions at TU/e in collaboration with Canon Production Printing, combining polymer science, rheology, and computational modeling to create the inks and coatings of tomorrow. Information
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stability as system strength continues to decline. Building on existing frameworks such as the WECC Composite Load Model (CLM), you will develop and validate data-driven methods for load identification and
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PhD project: Modelling Reliability and Resilience of Hydrogen Systems for Improved Safety and Sustainability Supervised by: Rasa Remenyte-Prescott (Faculty of Engineering, Resilience Engineering
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-informed machine learning (PIML) with domain-specific engineering knowledge. By embedding physical laws and corrosion mechanisms into data-driven models, the research will produce more accurate
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as early indicators of anthropogenic and climate-driven change. However, limited understanding of the processes shaping species’ biogeographic distributions constrains our ability to predict ecological
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) Data-driven surfactant identification and ii) Novel surfactant development. This PhD position is part of a multidisciplinary European consortium with high relevance to the biobased and biotech sectors
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performance. The research focuses on suitable domain models and the required model transformation, analysis, and synthesis approaches. We require candidates for PhD 2 to have good software engineering and
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of Electrical Engineering. Project description: Autonomous vehicles for mining and mobile robotics for industrial processes require complex motion planning as well as safe model-based predictive control and
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Job Description DTU, Department of Civil and Mechanical Engineering, the Section for Manufacturing Engineering invites applications for a PhD position (3 years) on the topic of simulation
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learning, as well as process-based models integrating thermal biology mechanisms. Funded by the ”Federal Ministry of Research, Technology and Space” (BMFTR), CSIDlab as part of the “Vector-borne disease