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prediction of regional climate and extremes using hybrid physics-AI models. About the project/work tasks There is a growing need for subseasonal-to-seasonal (S2S; 2 weeks to 12 months) predictions of regional
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. Understanding what variation persists, how populations evolve, and why responses differ among populations is important both for explaining diversity in nature and for predicting the evolutionary consequences
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). The position is part of the research project ThermRisk: A Personalized Digital Human Model for Predictive Thermal Risk Assessment. ThermRisk is a collaborative project between three faculties at HVL: the Faculty
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solutions for offshore wind turbines, enabling to enhance their structural awareness, real-time reliability assessment, and predictive maintenance decision support through integrated sensing, modelling, and
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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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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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solutions for offshore wind turbines, enabling to enhance their structural awareness, real-time reliability assessment, and predictive maintenance decision support through integrated sensing, modelling, and
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used to support real-time monitoring, predictive maintenance, geohazard detection, and safer railway operations. The initiative is a collaboration between the Department of Electrical Engineering, the
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infectious diseases in maternal, perinatal, neonatal and child health. More about the position The postdoctoral fellow will work at the interface of epidemiology, causal inference, prediction modelling