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18th September 2026 Languages English English English We are looking for a PhD Candidate in Laser-Induced Graphene (LIG) Sensors for Composite Materials Apply for this job See advertisement This is
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24th June 2026 Languages English English English The Department of Marine Technology has a vacancy for a PhD Candidate in multi-sensor fusion and representation learning for underwater robotics
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. Thus a background in fluid mechanics (marine hydrodynamics or aerodynamics) is required. Knowledge of measurement technology and relevant sensors, as well as industry experience from the industry
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for probabilistic unsupervised learning for structured biological data. The successful candidate will: Develop probabilistic factor models and scalable inference algorithms for structured biological (multi-view) high
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vendors remain difficult to identify and assess. These dependencies arise not only from technical limitations but also from interactions among algorithms, data flows, platform architectures, workflows, and
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predictive maintenance of ships and maritime systems. Modern vessels generate large amounts of heterogeneous operational data from sensors, machinery, control systems, maintenance records, and other sources
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for probabilistic unsupervised learning for structured biological data. The successful candidate will: Develop probabilistic factor models and scalable inference algorithms for structured biological (multi-view) high
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databases, information retrieval, data mining, or algorithms is expected. Excellent written and oral English language skills are required. The appointment is to be made in accordance with NTNUs guidelines
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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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databases, information retrieval, data mining, or algorithms is expected. Excellent written and oral English language skills are required. The appointment is to be made in accordance with NTNUs guidelines