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of innovative data- and machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods, machine learning
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civil and military operations“ and „operational analysis and evaluation“. The combination of these research focus areas provides an ideal platform for interdisciplinary research in simulation and
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“Scientific Empire: Infrastructure Control and Ideational Binding in China’s International Big Science Projects” (PI: Dr. Anna L. Ahlers). The project is part of the Research Unit “Learning Empire: Autonomy
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) Ability to teach in both German and English Ability to work independently as well as part of a team Fluency in scientific English (at least level B2 or comparable) Research interests in political violence
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, preparing seminars. Supportive tasks: - Editing of academic texts in English: copy editing and proofreading - Creating posters - Maintaining the e-learning pages (Moodle) and the bilingual department website
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partners with diverse professional and multicultural backgrounds Experience in working with AI applications or a willingness to learn how to use such systems Willingness to adopt flexible working hours, as
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you join us? In the field of quantum technology, the institute is working on the realization of a quantum computer based on color centers in diamond. We use nitrogen-vacancy (NV) centers as qubits
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of commitment, strong communication skills German language skills (B2), or commitment to acquire this level We offer: Research at the cutting edge: OPMEG is a novel technology with huge potential
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(www.cda.cit.tum.de ). Accordingly, we are currently searching for a Ph.D. student to join our team on AI for Chip Design! Our Research In our group, we create, modify, and adapt AI methods (such as Machine Learning
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Max Planck Institute of Microstructure Physics, Halle (Saale) | Halle, Sachsen Anhalt | Germany | 2 months ago
programming and numerical modeling Candidates do not need to have experience in all of these areas. Applicants with a strong background in electronic-structure calculations and an interest in learning quantum