94 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "Ulster University" uni jobs at Nature Careers in Germany
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The Faculty of Engineering at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) invites applications for an Assistant Professor of Machine Learning in Digital Health (salary group W1
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programme Biochemistry & Molecular Biology, and other relevant programmes at the University of Bayreuth. The ability to teach in English and German is expected. The general administrative requirements
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the Interreg project webpage ( https://www.sn-cz2027.eu/de/projekte/prioritat-2-klimawandel-und-nachhaltigkeit/100781629_beech ). For TUD diversity is an essential feature and a quality criterion of an excellent
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tools like ViennaRNA and NUPACK) and MD simulations (e.g., with GROMACS). Strong skills in statistical data analysis and machine learning in Python and R are expected, along with experience working in
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printing) and computer-assisted load analysis Expertise in the numerical simulation of realistic loading scenarios of the musculoskeletal system Your Profile: The future position holder has an academic
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postdoctoral research track record and are expected to build and lead a group to pursue a high-quality research programme. Current research interests within the Division of Condensed Matter Theory (https
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: https://uni-tuebingen.de/en/213700 Please submit your entire application using the web-based application platform https://berufungen.uni-tuebingen.de. The deadline for applications is 26 February 2026
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the development of the faculty's newly created research cluster “Biotic Interactions in the Anthropocene” (https://go.ur.de/biotic-interactions) are desirable. It is expected that the professorship will strengthen
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with the IfSG before starting employment (currently: measles). General information on professorships, hiring processes, and the German academic system can be found here: https://uni-tuebingen.de/en
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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 sending it as a