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the programme as a tandem Academic Requirements Predocs: graduates (Master's, Diploma or State Examination) from a German or international university in the transition phase at the end of their university studies
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Max Planck Institute for Mathematics in the Sciences | Leipzig, Sachsen | Germany | about 10 hours ago
. Close interaction with other research groups at the institute, in particular with the nonlinear algebra group of prof. Bernd Sturmfels, will be possible and encouraged. We are currently developing some
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programs, with three years funding, in collaboration with the University of Göttingen. Masters students aiming at a fast track PhD are also welcome. The Postdoc position is limited to two years with a
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Technical University of Munich, Centre for Mathematics Position ID: TUM -NUMMATHPOS [#25991] Position Title: Position Type: Postdoctoral Position Location: Garching, Bayern 85748, Germany [map
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scientists from all areas of the natural sciences, medicine, the history of sciences or any other disciplines covered by sections of the Academy are eligible to apply. Applications can be submitted up to 7
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Technical University of Munich, Centre for Mathematics Position ID: TUM -BWNUMMATH [#26301] Position Title: Position Type: Postdoctoral Position Location: Garching, Bayern 85748, Germany [map
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, the China Scholarship Council (CSC) and the DAAD have offered a joint programme to support Chinese postdocs since 2013. Who can apply? Outstanding graduates from Chinese Universities and institutes. What can
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- PhD student in quantitative verification interested in co-developing Automata Tutor - main developer of Automata Tutor Positions in the Formal Methods for Software Reliability group of TU Munich led by
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reduction of food web efficiency further directly impacts oceanic fish resources that play a major role in human nutrition. The critical role of aquatic life in biogeochemical cycles, for climate regulation
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Fritz Haber Institute of the Max Planck Society, Berlin | Berlin, Berlin | Germany | about 2 hours ago
project within the SusMax network focused on developing interpretable machine-learning frameworks for kinetic multiphase reaction-network discovery in the catalytic conversion of renewable feedstocks