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particular, geomagnetism) and the development of corresponding numerical methods. We offer the opportunity to work in a small interdisciplinary research group consisting of mathematicians, computer (geo
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, 2025 (stamped arrival date of the university central mail service or the time stamp on the email server of TUD applies), preferably via the TUD SecureMail Portal https://securemail.tu-dresden.de by
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Max-Planck-Institut für Kohlenforschung, Mülheim an der Ruhr | M lheim an der Ruhr, Nordrhein Westfalen | Germany | 2 months ago
description of intent, curriculum vitae, and certificates in electronic form to Prof. Dr. Claudia Weidenthaler (weidenthaler@kofo.mpg.de ), Max-Planck-Institut für Kohlenforschung, Kaiser-Wilhelm-Platz 1, 45470
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, which should include a cover letter, CV, academic transcripts, and contact information for two potential references, by email to kenichi.endo.2 at ipoc.uni-stuttgart.de or by post to the Institute
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submitted electronically no later than the 15th of June 2025 and be addressed to recruiting@senckenberg.de quoting the reference number 10-25005. For data protection information on the processing
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you join us? At our Heilbronn branch, we research and develop electronic digital circuits with the aid of artificial intelligence together with our partners from industry and academia. The focus is on
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scope of different subjects such as electrical engineering/electronics, microelectronics, microsystems technology, information technology, computer science, physics, chemistry, optics and manufacturing
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and the willingness to work in a team are expected. Our offer Work in a dynamic, international and interdisciplinary environment Close cooperation with computer scientists of the Research Group Data
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, and the names and addresses of two references incl. your earliest possible starting date) by email to bewerbung at igzev.de in pdf format by June 16, 2025.
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-edge Machine Learning applications on the Exascale computer JUPITER. Your work will include: Developing, implementing, and refining ML techniques suited for the largest scale Parallelizing model training