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criteria Machine Learning Expertise: A robust foundation in probabilistic modeling, Bayesian inference, deep learning, and/or anomaly detection Modeling & Simulation Experience: Familiarity with Building
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focus on combining simulations using spatial-genetic-demographic individual based models (e.g., using the software SLiM), machine learning approaches, and genomic data to estimate larval dispersal
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for analysing authentic, synthetic, and manipulated images or videos. The work will combine predictive performance with explainability, uncertainty estimation, robustness, and generalization. Attention will be
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-Computer Interaction (HCI) perspectives to investigate how AI companionship technologies may reshape experiences of care, companionship, emotional support, wellbeing, and autonomy among older adults. Your
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learning activities in courses related to communications and networking Required selection criteria You must have a relevant master’s degree in wireless communications and mobile networks, computer networks
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develop quantitative methods to estimate effects on infrastructure degradation, maintenance needs, operational risk, punctuality and costs. The aim is to develop and validate a practical, transparent
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other components. The PhD candidate will link TrainGate detections and early warnings with maintenance, incident, operational and cost data and develop quantitative methods to estimate effects
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with the same information and operational constraints. The project will also explore how AI can be used to approximate, accelerate, contextualize, or enhance optimization, for example by learning future
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CAD tools Finite-element analysis and simulation Rapid prototyping and additive manufacturing Signal processing and data analytics, control systems Machine learning or computer vision Product
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Department. About the project Corrosion represents a major economic burden, costing modern economies roughly 4% of their annual GDP. In Norway, this loss is estimated to be twice the total profits of