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PhD Position Simulating the Airspace of the Future: Fleet & Network Optimization for Future Aircraft
of twelve PhD candidates and three postdocs from across the Faculty of Aerospace Engineering, collaborating closely with experts at NLR and the Ministry of Infrastructure and Water Management to analyse
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in 4 larger programmes: Green Steels: The Dutch steel sector faces a major transition. The production, processing, use and recovery of steel is to be made significantly more sustainable by 2030 and
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processing or quantitative image analysis (e.g., microscopy or live-cell imaging data) is an advantage What we offer At Maastricht University, you’ll work in an international, open, and engaged environment. We
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analysis, is an advantage Experience with image processing or quantitative image analysis (e.g., microscopy or live-cell imaging data) is an advantage What we offer At Maastricht University, you’ll work in
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The PhD projects listed below are embedded in 4 larger programmes: Green Steels: The Dutch steel sector faces a major transition. The production, processing, use and recovery of steel is to be made
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for general criteria for the position. Preferred selection criteria Familiar with use of real-world data Experience with fieldwork Experience with signal and image processing Personal characteristics
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Circulatory Support (MCS) experimental lab with real-world clinical analysis in patients with cardiogenic shock and cardiothoracic surgery. You will work at the frontline where engineering, physiology, and
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trace element distributions at high spatial resolution. The resulting data will be integrated into thermodynamic models (e.g. FACTSage-based approaches) to support process optimisation and control in
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knowledge for a better world. You will find more information about working at NTNU and the application process here. About the position We have a vacancy for a PhD candidate in machine learning
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on genetically and optically tractable arthropod species that satisfy a number of appealing biological and technical requirements. We combine advanced microscopy and image analysis pipelines with genetic and