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chemistry, theoretical chemistry, molecular dynamics, data science, and machine learning are beneficial. What we offer: We offer a position with a competitive salary in one of Germany’s most attractive
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. - Neural networks and machine learning strategies for the analysis of scattering data. Large amount of scattering data obtained in our group requires development of the advanced analysis techniques. In
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of computational approaches to large scale simulations Basic knowledge of (geo)chemical processes and machine learning will be of advantage Expertise in Machine Learning approaches, ideally beyond neural networks
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defects of smectic-liquid crystal order in developing cross-striated muscle, or use machine-learning to expand existing custom-built image analysis pipelines (Python, Matlab). To learn more about this
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modern models beyond the worst case e.g. integrating machine learning into algorithm design. We are looking for candidates with a strong mathematical background, an excellent degree in mathematics
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the distribution of water vapor by means of machine learning approaches and to improve atmospheric correction beyond standard approaches. The research work is expected to contribute in two ways: (i) the separation
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the distribution of water vapor by means of machine learning approaches and to improve atmospheric correction beyond standard approaches. The research work is expected to contribute in two ways: (i) the separation
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mathematical modeling to simulate water fluxes and biogeochemical processes related to carbon and nitrogen cycling in the soil-plant system Experience with Bayesian inference and machine learning is an asset
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) in materials science, physics, chemistry, electrical engineering (or a similar discipline) with focus on sensorics; experience in data processing and machine learning; experience in 2D materials
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) in materials science, physics, chemistry, electrical engineering (or a similar discipline) with focus on sensorics; experience in data processing and machine learning; experience in 2D materials