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Organisation Job description The advent of modern machine learning (ML) methodology is accelerating scientific progress by streamlining research across disciplines. In chemistry, it enables
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containment. The prohibitively high computational cost of such simulations necessitates the development of efficient and robust surrogate models for general GCS modeling tasks, especially when inverse modeling
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of linear algebra and numerical optimization Understanding of statistical modeling and inverse problems is desirable Experience with programming languages like Python, MATLAB, or C++ Joy in dealing with
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focus on deep networks for solving inverse problems, learning robust models from few and noisy samples, and DNA data storage. The position is in the area of machine learning, with a focus on deep learning
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: Computational geoscientific skills in using geophysical and geological data for complex geological structure modeling. The research also requires skills in solving geophysical inverse problems. Demonstrated