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Field
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traffic demands increase, there is a growing need for innovative methods to continuously assess track condition and predict deterioration. This PhD project addresses this challenge by developing a novel
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with mainstream industrial software/systems (e.g., DCS, MES, APC). In-depth research in areas such as Reinforcement Learning, Large Language Models, Digital Twins, Predictive Maintenance. Overseas
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mental models, cognitive maps, and cognitive graphs. These approaches have provided important insights into how people perceive locations, learn route layouts, and understand spatial relations. However
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genetic perturbations into predictive models of tissue self-organization and repair. The project offers comprehensive interdisciplinary training in computational developmental biology and the opportunity
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objective is to develop methods that move beyond correlation-based prediction toward causal reasoning, intervention-aware modelling, and interpretable AI systems. This transition from correlation to causation
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. At the Division of Systems and Control , we develop both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and
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of Engineering. Where to apply Website https://www.jobbnorge.no/en/available-jobs/job/304536/phd-in-predictive-ai-base… Requirements Research FieldEngineeringEducation LevelMaster Degree or equivalent Additional
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3rd August 2026 Languages English English English We are looking for a PhD Candidate in Multi-scale, -physics, -fidelity wind modelling for wind farms Apply for this job See advertisement This is
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of flying birds to predict collision risk. This model will be fed by existing empirical data on bird flight behavioural responses to wind turbines from various bird radar studies. This model will allow
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an aerodynamic bird collision avoidance model, combining computational fluid dynamics (CFD) of the flow around wind turbines with the aerodynamic characteristics of flying birds to predict collision risk. This