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data analysis, statistical modelling, machine learning, life-cycle cost analysis, risk analysis or operations research The appointment is to be made in accordance with NTNUs guidelines for recruitment
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in one or more of the following: railway engineering, infrastructure maintenance, rolling stock, condition monitoring, sensor data analysis, statistical modelling, machine learning, life-cycle cost
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industries. Our research includes developing concepts for renewable-driven technologies and system solutions to decarbonize the chemical and energy industries, including carbon capture, utilization and storage
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of Marine Technology at NTNU has a vacancy for a PhD Candidate in Deep Learning enhanced FSI modelling of Multi-modular Floating Structures. The position is part of the AIMOS project (Artificial Intelligence
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. Knowledge of renewable energy systems (hydropower, wind, solar) and their grid integration. Experience in mathematical modeling, optimization algorithms, and data-driven methods. Possess a strong academic
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. Our research includes developing concepts for renewable-driven technologies and system solutions to decarbonize the chemical and energy industries, including carbon capture, utilization and storage
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. In Europe, these concerns have driven major regulatory developments, notably the EU AI Act and related digital sovereignty initiatives, which introduce risk- and rights-based approaches but often leave
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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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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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data-driven learning and which should remain within structured optimization. In line with AID’s research areas, the project will emphasize knowledge embedding, uncertainty representation, risk-aware