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for 3 - 6 years, with the option to enter the habilitation program in the format of 3 + 3 years. The salary will be determined according to the German collective wage agreement in public service (TV-L 13
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Collaborative Doctoral Project (PhD Position) - AI-guided design of scaffold-free DNA nanostructures
degree of independence and commitment Experience with machine learning and high-performance computing is advantageous, but not necessary Our Offer: We work on the very latest issues that impact our society
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to mastering the great challenges facing society today. The Institute of Resource Ecology performs research to protect humans and the environment from hazards caused by pollutants resulting from technical
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applications with large-scale AI, high-performance, and innovative computing. Within national and European projects, you will collaborate with our partners and drive the development of cutting-edge applications
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and operation of the relevant equipment and systems regular participation in project meetings and active exchange with partner institutions documentation, publication, and presentation of the scientific
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the art tool built upon the scientific machine learning environment (“SciML”) in the modern “Julia” programing language for high-performance symbolic-numeric computation in scientific computing and
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bridging satellite remote sensing applications with large-scale AI, high-performance, and innovative computing. Within national and European projects, you will collaborate with our partners and drive
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and data analytics (including machine learning and deep learning); from high-performance computing to high-performance analytics; from data integration to data-related topics such as uncertainty
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, they allow the use of metallic Li or Na as the anode. This can significantly increase the energy density of these batteries. In order to manufacture high-performance Na metal batteries, the compatibility
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QCLs) for high-resolution spectroscopy. Within the framework of the priority program INtegrated TERahErtz sySTems Enabling Novel Functionality (INTEREST) funded by the German Research Foundation (DFG