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will focus on designing computationally efficient, scalable, and adaptive AI models that operate under strict constraints in radio access, edge, and non-terrestrial network environments. The position is
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focuses on advancing data-driven and model-based methods for fault detection, predictive maintenance, and process monitoring. The successful candidate will conduct research in data-driven and model-based
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processes, targeting annual savings of £280,000. Responsibilities include creating and refining models to predict particle behaviour, calibrating them to 95% accuracy, and establishing sensor systems for real
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, health and environmental stimuli jointly determine how animals function, adapt and contribute to ecosystems. PhD: Development of AI Models for prediction of resilience and susceptibility infectious
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regression to represent unknown dynamics for model predictive control. Despite the practical success, there are still many theoretical open questions regarding scalability, uncertainty bounds and deriving
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, CRCF). It will develop AI tools to map and predict soil health across space and time, accelerate literature reviews, extract best management practices from long-term experiments, and design methods
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highly motivated PhD student to develop advanced fracture models for predicting material degradation and failure in additively manufactured steel in nuclear reactor water environments. The project focuses
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methods to understand and predict the adsorption, self-assembly, and protective behavior of N-heterocyclic carbenes (NHCs) on metallic and oxidized surfaces. NHCs are promising corrosion-inhibiting
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. The long-standing solar convective conundrum is the mismatch between convective flows inferred from helioseismology and flows predicted by state-of-the-art simulations. ReCon² addresses this by advancing
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topics such as statistics, high performance programming, machine learning and using data to constrain cosmological models. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs