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- NTNU - Norwegian University of Science and Technology
- NTNU Norwegian University of Science and Technology
- University of Oslo
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- University of Bergen
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- Western Norway University of Applied Sciences
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- NORWEGIAN UNIVERSITY OF SCIENCE & TECHNOLOGY - NTNU
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analysis (FEA) and strong skills in FEA software such as ABAQUS Hands-on experience in the construction and application of deep learning neural networks on material design by using PyTorch or Matlab PLEASE
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written and oral English language skills Extensive knowledge in analysis of Brillouin microscopy derived data Good bioinformatics skills Publications in peer-reviewed journals directly relevant to
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experimental work and data analysis. Personal characteristics To complete a doctoral degree (PhD), it is important that you are: Highly motivated. Able to work independently. Curious and enthusiastic. Strong
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. The objective of this PhD project is to develop AI methodologies for the analysis part of condition monitoring (CM) and predictive maintenance (PM). The primary challenge in predictive maintenance lies in
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members and more than 55 doctoral students and postdoctoral fellows. The Department conducts research at an international high level within the disciplines of algebra, analysis, didactics of mathematics
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methods, from action research and case studies to statistical analysis and operations research. Focus is put on multidisciplinary approach, joining skills, principles and methods of engineering, management
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develops specialized knowledge, applying mixed methodology, combining qualitative and quantitative methods, from action research and case studies to statistical analysis and operations research. Focus is put
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the maritime value chains. The objective of this PhD project is to develop AI methodologies for the analysis part of condition monitoring (CM) and predictive maintenance (PM). The primary challenge in predictive
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cycles and rapid transient stresses (e.g., short circuits and inrush currents). Accelerated ageing experiments under compression to study long-term deformation and material degradation. Analysis of fast