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Mehdi Shafiei Joud earned a Ph.D. in Satellite Geodesy and has made significant contributions in developing methods for relativistic geodesy using optical-atomic clocks. He has applied machine learning techniques to assimilate satellite data into geophysical models, with a focus on Post-Glacial Rebound research. His work has involved the development of a pure gravimetric model for isostatic adjustment using unsupervised machine learning methods for data processing ensemble techniques. Extensive experience in satellite gravity missions such as GRACE has allowed him to understand mass transport processes in the Earth system. His research aims to integrate Synthetic Aperture Radar (SAR) interferometry with satellite gravity data using machine learning techniques to enhance real-time environmental monitoring. A key aspect of his work is the development of Real-Time Earth Monitoring Prediction Systems (REMPS), which employ big data analytics and computational advancements to predict effects of crustal stresses, seismic hazards, and changes in water resources and sea levels, thereby contributing to accurate predictions of land subsidence and climate-related risks. Beyond research, he is passionate about educating future scientists, teaching geodesy and remote sensing. His expertise aligns with the United Nations Sustainable Development Goals.
Requirements apply generally to Master's programs across departments including Sociology, Business, and Engineering at Aalborg University.