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offering PhD positions for students with a background in data science, computer science, computational science, or a domain science with a strong focus on computational science and an interest in training
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funding phase at the Martin Luther University Halle-Wittenberg will start with a highly interdisciplinary and ambitious research program in November 2025. We invite applications for 8 PhD positions for 3
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industry. The center offers an excellent working environment with access to state-of-the-art technologies and an outstanding high-performance computing infrastructure. For TUD diversity is an essential
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positions in the areas of biochemistry, structural biology, biophysics, cell biology, systems biology, and computational biology. We are looking for outstanding graduates from all over the world to join our
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1,000 employees from over 50 nations, it is the largest institute of the Max Planck Society . The Research Group Computational Biomolecular Dynamics (Prof. Dr. Bert de Groot) is inviting
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Description The Chair of Applied Mathematics at the Faculty of Mathematics and Geography at the KU Eichstätt-Ingolstadt invites applications for a position as Doctoral candidate in Applied Mathematics/ Approximation Theory to be filled by the earliest possible starting date. The Chair of Applied...
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that demand interdisciplinary solutions? Then the Program for Collaborative Doctoral Projects is the perfect opportunity for you. Many of today’s most pressing problems can only be tackled through
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that demand interdisciplinary solutions? Then the Program for Collaborative Doctoral Projects offers a perfect opportunity for you. Today’s most pressing problems can only be solved in cross-institutional
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that demand interdisciplinary solutions? Then the Program for Collaborative Doctoral Projects is the perfect opportunity for you. Many of today’s most pressing problems can only be tackled through
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and performing laboratory (wind-wave facility) experiments, using state-of-the-art imaging techniques developing computational codes to process and understand large experimental datasets (e. g., image