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Field
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years with both research and other career-promoting work. This will be clarified in the recruitment process. A career plan will be prepared that specifies the competencies that the Research Fellow will
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at the intersection of construction and digitalization: optimization of construction processes, new and alternative cementitious materials, sensor-based monitoring of concrete performance, and
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products to prevent, diagnose, treat, or cure disease in donors and patients. Research activities cover the full spectrum of early inventions, development into novel products and services, donor studies, and
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PhD Position Simulating the Airspace of the Future: Fleet & Network Optimization for Future Aircraft
current and future aircraft models + Evaluating how these differences affect fleet assignment and network planning + Optimizing airline operations (e.g., fleet composition, scheduling, routing) based on new
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aromatics and specialty chemicals. In this project, you will design and study catalytic processes that upgrade complex phenolic mixtures into targeted products using advanced bifunctional catalysts. By
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includes system operation, continued development, and performance optimization. The successful candidate will collaborate closely with the quantum metrology group at the University of Bonn and the geodetic
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information processing into an established consortium of experimental physicists and cancer biologists. The project establishes a close collaboration between IEMN and PhLAM, with the support of CANTHER
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, 9,000 employees 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
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PhD Position Simulating the Airspace of the Future: Fleet & Network Optimization for Future Aircraft
: Model development: + Characterizing the key performance differences between current and future aircraft models + Evaluating how these differences affect fleet assignment and network planning + Optimizing
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optimization of machine learning methods for processing Quantum OCT (Q-OCT) signals. Design, training, and testing of neural network architectures for artefact and dispersion removal in Fd-Q-OCT and SS-Q-OCT