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, specifically pertaining to the university’s master's program in mathematical physics and to undergraduate mathematical education, as well as moderate responsibilities in academic self-administration such as
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wide range of markets such as automotive, industrial and aerospace. The rapidly growing demand for computing power especially in relation to quantum computing (QC), artificial intelligence (AI), and
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profound knowledge in computational and theoretical physics/chemistry. Capability of team work is essential. Skills in high-performance computing, materials chemistry, theoretical chemistry, molecular
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Your Job: We are looking for a highly motivated postdoc to join the neutron backscattering spectrometer team to support the instrument development program for the instrument SPHERES. To further
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significant physical and mathematical components. It is essential that you demonstrate enthusiasm for collaborating in a team and for contributing to the innovative IOW research program (2024–2033), see https
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the Department of Physical Oceanography for the next 4 years (until 31.12.2029) and a percentage of 100% (40h/week), subject to the funding of the project. Remuneration is paid in accordance with the Tarifvertrag
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research environment. Access to state-of-the-art equipment and facilities. Member of the integrated Research Training Group for dual mentoring, comprehensive doctoral training program, weekly seminars
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Max Planck Institute for Plasma Physics (Garching), Garching | Garching an der Alz, Bayern | Germany | 19 days ago
Job Code: 25/027 Job Offer from July 15, 2025 The Max Planck Institute for Plasma Physics (IPP) is one of the world"s leading centers for fusion research. With around 1100 dedicated employees in
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Your Job: Development of a physical solid-state cathode model and simulation of cathode performance Mathematical optimization of the model parameters using laboratory measurements Preparation and
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of neural hydrology, where hydrological models are directly learned from data via machine learning (e.g., LSTM neural networks, [1]). Initially, these models ignored all physical background knowledge and did