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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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modern control design methods Experience with modelling and simulation of dynamic systems Programming proficiency sufficient for implementing and testing control algorithms Experience with nuclear reactor
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, through course information or thesis deliverables in the application Skilled in either computational modelling or experimentation, paired with the willingness to work on both Personal characteristics
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during the selection process: Good understanding of Software Engineering or Systems Engineering fundamentals Experience with modeling of software and/or system architecture Experience with agentic
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authenticity of visual media and provide understandable evidence for model decisions. The candidate will investigate how general-purpose pretrained visual and multimodal representations can be adapted
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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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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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laboratories. The topic of the doctoral research will on development of efficient and scalable search engines. This entails design of new retrieval models, efficient query processing, and use of modern hardware
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will be the Head of Department. About the project This PhD position is part of the WP4 Digital twin and asset management concerning multiscale wind simulation that involves modelling wind flow across
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. The topic of the doctoral research will on development of efficient and scalable search engines. This entails design of new retrieval models, efficient query processing, and use of modern hardware