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Group , a leader in innovative multi-sensor atmospheric remote sensing from ground, airborne, and satellite platforms. Our group develops advanced algorithms and data analysis methods to address
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on electrochemistry, atomic scale and multi-physics modelling, autonomous materials discovery, materials processing, and structural analyses. We also focus on educating engineering students at all levels, ranging from
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Post-Doctoral Associate in Sand Hazards and Opportunities for Resilience, Energy, and Sustainability
-Lagrangian (CEL), Material Point Method (MPM), or advanced Finite Element Methods). Physical modeling of tunnel excavation and ground response (e.g., geotechnical centrifuge testing, lab-scale TBM experiments
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Group , a leader in innovative multi-sensor atmospheric remote sensing from ground, airborne, and satellite platforms. Our group develops advanced algorithms and data analysis methods to address
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experience in developing competitive national and international research applications Experience in programming languages (e.g. Python/R) and analytical skills in model evaluation Experience in advanced
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in Center of Excellence in AI for Structures, Prognostics & Health Management within the Faculty of Aerospace Engineering at TU Delft. The group advances AI-based SHM, prognostics and health management
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for production improvement. Explore, build and validate a low fidelity digital twin including more advanced high fidelity configurations critcal for scaling and improvement. Calibrate the models inside the digital
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committed to advancing equality and we aim to ensure that our culture is inclusive, and that our systems support flexible and family-friendly working, as recognized by our Juno Champion and Athena SWAN Silver
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expertise in forest ecology, disturbance ecology, and landscape ecology, and methodological expertise in harmonizing distinct databases (e.g., forest inventory, remote sensing, land cover), GIS, and R-based
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into conservation and ecosystem restoration to advance outcome success. We test our research questions with multi-factorial, field experiments and complement that approach with modeling, meta-analysis, and/or