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Field
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will be implemented and validated in commercial finite element (FE) software. The resulting FE model will define the digital twin of the welded structure and provide the basis for the second PhD project
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4-year PhD fellowship in the Research Programme - Deep Learning-Accelerated Crystallography Pipeline
mathematical aspects; Adequate programming skills and willingness to apply machine learning where required; Optionally, previous experience contributing to open-source scientific software. Terms of employment
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-source scientific software
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, digital communications, wireless networking and software development through hands-on experimentation. The research offers the opportunity to contribute to cutting-edge technologies for future cyber
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SINTEF. The project aims to develop an automated high-throughput platform for physiologically relevant cell research by combining robotics, microfluidics, advanced sensors, cloud-connected software, and
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relevant cell research by combining robotics, microfluidics, advanced sensors, cloud-connected software, and artificial intelligence. The successful candidate will become part of a multidisciplinary
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detailed knowledge of the performance parameters that affect their application software, as it aids in making future technology choices, predicting performance and scalability, and adapting critical software
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of the performance parameters that affect their application software, as it aids in making future technology choices, predicting performance and scalability, and adapting critical software to new developments
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damage development as a function of fatigue loading. The models will be implemented and validated in commercial finite element (FE) software. The resulting FE model will define the digital twin
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MRI, high-resolution confocal microscopy and electron microscopy. Develop and refine imaging analysis pipelines, using appropriate software, quantitative approaches and innovative analytical methods