95 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "UCL" 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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will also profit from the vibrant research community around machine learning of the SCADS.AI center (https://scads.ai ) and the recently granted Excellence Cluster REC² – Responsible Electronics in
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Biochemistry (IPB, https://www.ipb-halle.de/en/ ) the following position is to be filled ideally on October 1st, 2027: Full Professor (Chair, W3) of Chemistry of Natural Products and Bioactives und Director at
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The Center for Molecular Bioengineering ( B CUBE) ( https://tu-dresden.de/cmcb/bcube ) and its partner institutions, the Biotechnology Center (BIOTEC) and the Center for Regenerative Therapies
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stamp on the email server of TUD applies), preferably via the TUD SecureMail Portal https://securemail.tu-dresden.de by sending it as a single pdf file tolinda.petersohn@tu-dresden.de or to: TU Dresden
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exclusively in electronic form and in accordance with the “Applicant Guideline Template” and the “Applicant Form” of the Medical Faculty Mannheim for W3 professorships (see https://www.umm.uni-heidelberg.de
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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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submitted online via the appointment portal of Heinrich Heine University Düsseldorf: https://berufungsportal.hhu.de If you have any questions regarding the position, in particular concerning the bioeconomy
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investigations as well as analyzing and interpreting the results of these investigations developing and testing innovative spinning technologies and modifying existing machine technology preparation of scientific
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Design and use of data spaces and digital twins for materials and autonomous material laboratories Use of deep learning methods to connect theory, simulation, and experiments Integration of high throughput