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Website https://www.fz-juelich.de/portal/home Street Wilhelm-Johnen-Straße Postal Code 52428 E-Mail info@fz-juelich.de Phone +49 2461 61-0 Fax +49 2461 61-8100 STATUS: EXPIRED X (formerly Twitter) Facebook
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/home Street Wilhelm-Johnen-Straße Postal Code 52428 E-Mail info@fz-juelich.de Phone +49 2461 61-0 Fax +49 2461 61-8100 STATUS: EXPIRED X (formerly Twitter) Facebook LinkedIn Whatsapp More share options E
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was established in 2019 with our partner institute, the Nagoya Institute of Technology, Japan, in order to better understand lead-free perovskite materials for photo-electro-mechanical energy conversion systems
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Leibniz-Institute for Plant Genetics and Crop Plant Research | Neu Seeland, Brandenburg | Germany | 14 days ago
-gatersleben.de Street Corrensstraße 3 Postal Code 06466 STATUS: EXPIRED X (formerly Twitter) Facebook LinkedIn Whatsapp More share options E-mail Pocket Viadeo Gmail Weibo Blogger Qzone YahooMail
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | about 1 month ago
Job Code: 21-25 Job Offer from July 31, 2025 The Max Planck Institute for Multidisciplinary Sciences is a leading international research institute of exceptional scientific breadth. With more than
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“DiamondNanoNMR” we are looking for ambitious PhD students (75%, TV-L E13, limited to 3 years). Our mission is to apply quantum information concepts to nanoscale sensing. This emerging technology stands
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nanoconstructs. The primary goal is to build an AI-based digital twin, which can predict the assortment and sequence of two-dimensional all-DNA motifs required for the engineering desired tessellation patterns
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Your Job: The department of Electrocatalytic Interface Engineering, headed by Prof. Dr.-Ing. Simon Thiele, focuses on the development, characterization and testing of electrochemical systems from
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theories and numerical methods, carrying out and analysing field and remote sensing observations and conducting and analysing numerical model simulations. The PhD position is funded by the German Research
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hydrological community. Alternatively, it is well known that Neural ODEs [2] are capable of representing dynamic systems that are coded in ODEs. They can encapsulate the complex temporal dependencies and