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UiO/Anders Lien 4th October 2026 Languages English English English 3-years PhD position in probabilistic machine learning and statistics Apply for this job See advertisement About the position We
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benchmark chemometric and physics-informed machine learning models to monitor, forecast, and ultimately control critical process parameters, implanting these models in advanced control frameworks to optimize
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PhD Candidate to conduct research on Artificial Intelligence for managing Shipbuilding Supply Chains
knowledge of AI-enhanced planning in shipbuilding supply chains. Apply quantitative methodologies, such as simulation, analytical modelling, and AI‑driven techniques, to develop decision support for efficient
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Norwegian University of Life Sciences (NMBU), within the Faculty of Science and Technology(REALTEK), invites applications for a PhD position in Applied Causal Machine Learning. The position is affiliated with
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PhD Candidate to conduct research on Artificial Intelligence for managing Shipbuilding Supply Chains
methodologies, such as simulation, analytical modelling, and AI‑driven techniques, to develop decision support for efficient planning and coordination of production activities in supply chains and generate
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also interested in environmental data, AI-powered analytics, applied mathematics and physics? Then this job is for you! The goal is to complete a doctoral education while working on a collaborative
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. We are seeking a candidate motivated to explore the how emerging technologies – such as machine learning, generative AI, and extended reality (XR) – impact societal preparedness planning required
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or bioprocessing. Experience with analytical methods used to investigate gene expression, proteins or metabolites. Experience with systematic experimental design or design–build–test–learn workflows. Personal
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high-frequency power conversion technologies through analytical modelling, multi-domain optimization, hardware development, and experimental validation. The research will explore the interaction between
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) the use of AI tools in the health professions (clinical decision-making, professional responsibility, effects on trust, patient–provider relations, etc.). We welcome empirical, analytical, normative, and