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investigate the possibility of using microbial biosignatures to identify sites with past and current hydrocarbon leakage by developing new machine learning models in collaboration with the research group
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exam before 15.06.2026. It is a condition of employment that the master's degree has been awarded. Background in optimization is required. Experience in machine learning is an advantage. Familiarity with
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implement new nonlinear iterative solvers, with the goal of exploiting models of various complexity, ranging from high-performance computing, via reduced-order models to data-driven (machine-learned
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solvers, with the goal of exploiting models of various complexity, ranging from high-performance computing, via reduced-order models to data-driven (machine-learned) representations. In particular, we
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understanding of how acoustic waves are generated and transmitted in wells. The LeDAS project aims to overcome these challenges by combining physical modelling, advanced signal processing, and machine learning in