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- NTNU - Norwegian University of Science and Technology
- NTNU Norwegian University of Science and Technology
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computational image analysis, computer vision and machine learning. The aim is to develop robust and standardized methods to link structural, mechanical and biological properties to biomaterial performance and
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, quantitative image analysis, data processing and machine-learning-based modelling. More about the position The main purpose of the fellowship is research training leading to the successful completion of a PhD
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November 2026 at 23:59 CET Expected start: 1 January 2027, or upon agreement About REGULAIRE The scholarship is part of REGULAIRE (Regulatory Learning for the Governance of Transformative Technologies), a
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quantitative analysis is welcome. You are not expected to have experience in all these areas. We are looking for strong analytical and academic writing skills, the ability to work independently and
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example through relevant work experience and/or peer-reviewed academic works. You must have excellent written and oral English skills You must have good programming and data-analysis skills, for example in
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Analysis and Computational Methods: Develop and apply computational pipelines for processing large-scale imaging datasets, integrating structural and functional data, and identifying organizational
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and 43,000 students work to create knowledge for a better world. You will find more information about working at NTNU and the application process here. ... (Video unable to load from YouTube. Accept
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analysis. Your work Examine how delegating work to AI affects leaders' work, team dynamics, AI overreliance and disclosure, implicit theories and mindsets related to digital teams, and critical scrutiny
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development with field or team studies is plausible after discussion with the supervisory team about samples, settings, methods and analysis. Your work Examine how delegating work to AI affects leaders' work
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, signal processing, or data-driven security analysis. Previous research or experimental experience in hardware/embedded-system security, demonstrated through a thesis, project, publication, or relevant work