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approach of data-driven membrane discovery that includes material space construction and exploration, candidate selection and verification, providing data for machine learning models to optimise membrane
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the Centre for the Study of Professions. Programme description Information about the content and structure of this programme is described in more detail in the programme description (student.oslomet.no) PhD
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our understanding of IAI mechanisms and develop innovative antibacterial biomaterials to improve patient outcomes. Structured around three core scientific pillars—regenerative medicine, biomaterial
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control Advance the knowledge on grid protection, control and automation developments The main supervisor of the PhD candidate will be Professor Irina Oleinikova . Co-supervisors will be Professor Hans
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Technology » Energy technology Environmental science Computer science » Modelling tools Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country Norway Application Deadline 31 Oct 2025
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of smart technologies to visualize yard operations in a digital form (such as virtual models and digital twins). Smart technologies can collect, analyze, and represent data from various sources
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production. The technological advances within cost-effective drilling have their starting point in the fracking industry, and other oil and gas industries. Mainland Norway consists mostly of igneous and
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, there is a significant need for technological advances and increased competitiveness to drive the growth of offshore renewable energies, and energy storage in the coming years. Within this context
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of physics into machine learning and deep learning architectures to create accurate, physically consistent, efficient and interpretable/generalizable models. This PhD project will contribute to the development
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models to resolve blade loads and structural responses under both operational and extreme conditions, including scenarios with partial out-of-water exposure Uncertainty quantification to ensure robust and