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20th September 2026 Languages English English English The Department of Materials Science and Engieering has a vacancy for a PhD Candidate in large language models(LLMs) for data extraction and
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evaluate large language models (LLMs) assisted methods for extracting, organising and assessing complex scientific information related to materials. The initial application will be phase diagrams and related
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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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30th September 2026 Languages English English English We are looking for a PhD candidate in Structure-preserving Generative Modeling Apply for this job See advertisement This is NTNU NTNU is a broad
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companies. The research will integrate techniques of numerical analysis and structure-preserving algorithms to generative modeling in AI. It will build upon the work done at IMF and SINTEF in this field. We
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the field, model development, remote sensing analyses, analyses of timber production, economic and policy data and documents, development of forestry cost functions, or surveys of forest owners. The project
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. The research may involve a combination of commercial process simulation tools and in-house model development for individual unit operations and integrated systems. Hybrid modeling approaches combining first
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. The research may involve a combination of commercial process simulation tools and in-house model development for individual unit operations and integrated systems. Hybrid modeling approaches combining first
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engineering, control engineering, managerial economics with strong quantitative skills, or physics or mathematics with a specialization in operations research. Your course of study must correspond to a five
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, natural resources management, and model-based learning and communication. The group makes use of a variety of tools and techniques: stakeholder mapping, governance analysis, participatory modeling, causal