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data, and how we use that information to predict the genetic and demographic responses of spatially structured marine populations to environmental variation and change. Specifically, the primary focus
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Norwegian Mechanistic Empirical pavement design system. The project will combine material characterisation, theoretical modelling and experimental validation to improve the prediction of pavement performance
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Strong background in control, robotics and machine learning, and experience in areas like model predictive control, adaptive control, reinforcement learning, robot perception or manipulation. Programming
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-enhanced model predictive control, control-theoretic safety and stability guarantees for learning-enabled systems, and the integration of foundation models with autonomous robotic decision-making and control
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detailed knowledge of the performance parameters that affect their application software, as it aids in making future technology choices, predicting performance and scalability, and adapting critical software
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PhD in Advanced Testing, Modelling, and Simulation of Composite Materials under High Velocity Impact
impacts. The candidate will develop and validate computational models capable of predicting penetration, damage evolution, and energy absorption, while exploring novel lightweight material configurations
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the timing of seasonal spring melt onset; and 3) Improve melt onset detection and prediction, using information from previous ROS events, using multi-frequency SAR and altimetry observations. If time allows
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quality (e.g., Mn contamination in Longyearbyen) and nutrient export to coastal ecosystems. Develop a predictive understanding of water quality changes under ongoing permafrost degradation. The four-year
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on predicting their service life and failure mechanisms. The research will be carried out in close collaboration with fellow researchers and industrial partners within the research area. For a position
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of the performance parameters that affect their application software, as it aids in making future technology choices, predicting performance and scalability, and adapting critical software to new developments