78 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "U.S" uni jobs at Nature Careers in Germany
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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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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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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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to actively shape its holistic development (https://www.tu-braunschweig.de/hochschulentwicklung ). In addition to the classic service areas of a university (research, teaching and studies, transfer
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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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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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W3 Endowed Professorship for “Hemodynamic Modeling in Atherosclerosis- (f/m/d) KSB Foundation W3 end
exclusively in electronic form and in accordance with the “Applicant Guideline Format Template” and the “Applicant Form” of the Medical Faculty Mannheim for W3 professorships (see https://www.umm.uni
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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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for three letters of reference to be submitted via https://www.pks.mpg.de/reference . Please submit all materials by April 3rd, 2026. Apply now Prof. Dr. Frank Jülicher Max Planck Institute for the Physics
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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