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students and is rapidly expanding its range of education and research activities. We are looking for a Research Fellow to develop innovative geophysical imaging methods and applying it to underground
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of undergraduate and graduate students Job Requirements: PhD’s degree in seismology and solid geophysics At least 2 years of relevant experiences in machine learning and earthquake monitoring Familiarity with
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Sensing (DAS) data processing and compression using ML Physics-driven machine learning for geophysical modeling and inversion Thus, the candidate is expected to have or about to have a PhD in a relevant
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surrogate model for giant impacts trained on SPH simulations, and then to analyse the results in the context of the geophysics, geochemistry and dynamics of the terrestrial planets. For more information and
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with AI, advanced data analytics, and the management of large geophysical datasets to support model analysis and evaluation. The ideal candidate will possess strong communication skills, evidenced by
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). The successful candidate will focus on designing and conducting large scale laboratory and field trials using a range of geophysical equipment such as Ground Penetrating Radar or gravimeters (e.g. Scintrex CG6
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, University of Oslo, within the field of geological carbon storage, with a particular focus on in-situ CO₂ mineralization and the geological, geophysical, petrophysical and rock physical characterization
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the context of the geophysics, geochemistry and dynamics of the terrestrial planets. What distinguishes this approach is its focus on post-giant-impact systemwide dynamics, and the exchanges between planets
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on SPH simulations, and then to analyse the results in the context of the geophysics, geochemistry and dynamics of the terrestrial planets. What distinguishes this approach is its focus on post-giant
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. This fellowship offers a unique opportunity to be at the forefront of groundwater technology, gaining experience on projects ranging from groundwater sampling and geophysical imaging in the field, analysis