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changer for the energy transition — or it could create new challenges. The extent to which it will influence future production systems, material flows, and energy systems is not yet fully understood
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in the subinstitute Physics of Nanoscale Systems (ER-C-1) , which focuses on developing novel methods to understand material properties at the atomic level. Your Job This PhD project investigates
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Innovation in science, and in quantum-, nano- and bio-technologies depends on a rigorous understanding of the inner workings of custom-tailored materials. Since the elementary building blocks
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of Dirac electrons in magnetic 2D materials, to give a few examples. We offer: * Structured doctoral training, international visibility, and participation in joint workshops and conferences
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conference, or other results of the starter funding. These success criteria will be evaluated during the selection process for the Booster Grant. Please see the application guidelines for details
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evaluated during the selection process for the Booster Grant. Please see the application guidelines for details. International Excellence – Programme Goals The Centre supports academic exchange across
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Antarctic Research, combines geochemical analyses, 16S rRNA gene sequencing, qPCR, and RNA-based stable isotope probing (RNA-SIP) using 13C-labelled organic material. We aim to identify these key fermentative
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-conversion and is closely connected to the Munich Center for Quantum Science and Technology (MCQST), providing an interdisciplinary environment linking condensed-matter physics, quantum theory, and materials
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University Bochum: scholarship of 2,050 EUR per month Academic admission requirements Excellent Master’s degree in materials science, physics, engineering or chemistry We particularly encourage applications
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Heidelberg Institute for Theoretical Studies (HITS gGmbH) | Heidelberg, Baden W rttemberg | Germany | about 5 hours ago
) in Geometric Deep Learning to join the Machine Learning and Artificial Intelligence (MLI) g roup to perform research in geometric deep learning for materials science. This research is part of