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Field
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time. After the interviews of the central IMPRS application call, successful candidates start their PhD projects between June and December. Successful candidates who applied to individually advertised
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in physics, electrical/electronic engineering, computer science, mathematics, or a related field Strong background in machine learning, particularly deep learning and optimization methods Excellent
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degree in computational engineering, mechanical engineering, computer science, applied mathematics, physics or a similar area very good programming skills in Python good prior experience with neural
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status under the Geneva Convention are not taken into account Demonstrate their ability to understand English and express themselves fluently in speech and writing Please feel free to apply
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the Applied and Computational Mathematics group. His research focuses on time integration methods for all types of (coupled) dynamical systems, with a focus on port-Hamiltonian ODEs, DAEs and PDEs with
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welcomes applications from individuals with a master’s degree in the following fields: Computer Science Mathematics Natural or engineering sciences or an equivalent degree, interested in High-Performance
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(physics, mathematics or geosciences) Experience in numerical modelling of geodynamic processes and/or continuum mechanics It would be an asset to have command expertise in modelling the interlink between
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partnership with the German Federal Waterways Engineering and Research Institute (BAW); building a fully coupled model that simulates surface hydraulics and subsurface flow with relevant turbulence models and
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psychology A successfully completed scientific university degree in psychology Accurate working and strong team spirit Confident handling of large, longitudinal data sets Competent use of statistics and data
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by interaction with its application and, in turn, is key to progress in technology and science. In Göttingen's mathematical history, this is exemplified by Carl Friedrich Gauss, who was inspired by his