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Field
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mechanisms occurring in these materials and their synthesis over all relevant length scales (e.g., cutting-edge ab initio methods, atomistic simulation methods, multi-scale modelling, machine learning) High
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2019, unites top PhD students in all areas of data-driven research and technology, including scalable storage, stream processing, data cleaning, machine learning and deep learning, text processing, data
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is currently the main focus. Here, laboratory experiments are usually combined with state-of-the-art methods such as optogenetics, connectomics or machine learning. Activate map To activate the map
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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 synthesis and
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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 research environments. TUD is one of eleven
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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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positions) : the deadline depends on the respective advertised open positions. Tuition fees per semester in EUR None Combined Master's degree / PhD programme No Joint degree / double degree programme No
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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 this project
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Degree PhD Course location Göttingen Teaching language English Languages English (100%) Programme duration 6 semesters Beginning Only for doctoral programmes: any time Application deadline Please
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://www.uni-muenster.de/Geoinformatics/en/Studies/study_programs/PhD/application/index.html Tuition fees per semester in EUR None Combined Master's degree / PhD programme No Joint degree / double degree