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cells with improved energy and power density, longer lifetime, and maximal safety. Find out more about our mission and future-oriented projects here: https://www.fz-juelich.de/en/iet/iet-1 We
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(including cover letter, CV, diplomas/transcripts, etc.), which you can submit via our online-application-system: https://www.hzdr.de/db/Cms?pNid=490&pLang=en&pOid=76369
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e.g. Cell Culture, Omics approaches or Molecular Biology Basic programming skills (e.g., Python or R) or motivation to learn Fluency in English is required; German skills are an advantage, primarily
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applies), preferably via the TUD SecureMail Portal https://securemail.tu-dresden.de by sending it as a single pdf file to mlcv at tu-dresden.de or to: TU Dresden, Chair of Machine Learning
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Python or C/C++. The candidate should have an interest in developing novel bivariate methods in machine learning for molecular property prediction within an interdisciplinary application. Ideally
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courses or doctoral candidates Selection Selection is made by the National Science and Technology Council - NSTC Further information National Science and Technology Council - NSTC https://www.nstc.gov.tw
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, Andrea.Iris.Schaefer at kit.edu , https://www.iamt.kit.edu Applications Please send applications with CV, publication list and your contribution to the publication (if relevant), academic transcripts, degree
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, and/or numerical mathematics, as well as an excellent command of a programming language, preferably Python or C/C++. The candidate should have an interest in modeling and solving a complex, coupled
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, simulation, and software development (e.g., Python, C++, ROS, MATLAB) analytical skills and ability to combine theoretical modeling with experimental validation We are looking for first-class graduates with
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19 Dongfang Donglu, Chaoyang District 100600 Beijing, VR China Tel.: +86 010/6590 6656 Fax.: +86 (10)/6590-6393 E-Mail: postmaster@daad.org.cn WWW: https://www.daad.org.cn/zh Please note Your