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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 intersection of acoustics, physical modeling, voice and health. During the PhD you will build a sought-after profile spanning acoustics, signal processing and clinical application, in strong demand in both
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educational curriculum in computational chemistry and modelling at the Department of Chemistry. The position offers a unique opportunity to be part of an internationally recognized group and to develop
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the PhD position. The topic of the PhD fellowship is at the interface of numerical mathematics, agentic and generative AI models, and computer science. A successful candidate will be offered a three-year
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3rd August 2026 Languages English English English We are looking for a PhD Candidate in Measuring Uncertainty and Risk with Agentic AI for Decision-Oriented Modelling Apply for this job See
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candidate will help design, implement, and evaluate a novel science-policy engagement process that combines climate modeling, role-play simulations, future storytelling, and participatory workshops
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satellite imagery are fused with land-surface models using data assimilation. The goal is to develop an adaptive experimental design frame-work for the observing system to guide ongoing measurement campaigns
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of future reactor systems with a focus on systems relevant for Norway. The objective is to further develop and validate machine-learning surrogate models derived from high-fidelity multiphysics simulations
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good understanding on the numerical methods used in ice sheet dynamics, experience in code development and in benchmarking ice sheet models. All candidates and projects will have to undergo a check
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-efficiency requirements at the energy edge. Further, you will incorporate compliance-by-design AI architectures and models and validate our solutions across key energy use cases such as energy market