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for the definitive project description, eligibility requirements and application details. DC01: Building Problem Sensing Capacities Through Portfolio Analysis Host university: University of St. Gallen, Switzerland
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combining catalyst development, reaction engineering, and product analysis, you will contribute to new routes for turning renewable carbon into high-value chemicals. The project is part of the collaborative
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security in mind from the outset. Your research will combine cryptographic design with formal security analysis, helping to bridge the gap between theoretical security and secure real-world implementations
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main responsibilities will be shared between two main research activities (approximately 70%-30%): Large-Scale Survey Development & Data Analysis for Ex-Post Assessment: Develop and deploy a structured
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for a highly motivated PhD student to join the Institute of Mechanics. Our research focuses on the analysis of dynamical effects in various applications, ranging from train dynamics to biomechanics. Our
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performance simulation, lifecycle assessment, or climate resilience. Knowledge of uncertainty quantification and risk modelling is desirable. You have experience with programming and data analysis (e.g., Python
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that generalize across different physical settings. Building on this motivation, the project focuses on the definition, development, and analysis of scientific foundation models: large-scale, generalizable models
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to maintain UK warfighting readiness in an increasingly dangerous and complex world. As MOD science and technology leaders, Dstl provides expert advice, analysis and capability across a wide range of
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role in enabling this transition, while also introducing new challenges related to system dynamics, stability, and control. This project focuses on the development of modeling, stability analysis
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, or supervised/unsupervised learning depending on the available data) using spatial analysis and geographic machine learning tools (e.g., scikit-learn, PyTorch/TF + GeoPandas/Shapely) - Implementing a semantic