209 algorithm-"Prof" "NTNU Norwegian University of Science and Technology" PhD positions in Germany
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Hannover Institut für Erdsystemwissenschaften / Abteilung Bodenkunde / Naturwissenschaftliche Fakultät Prof. Dr. Stephan Peth Address StreetHerrenhäuser Str. 2Zipcode30419CityHannover Contact details Tel:+49
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or to TU Dresden,Professur für Theoretische Chemie, Herrn Prof. Thomas Heine, Helmholtzstr. 10, 01069 Dresden, Germany. Please submit copies only, as your application will not be returned to you. Expenses
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learning (ML) methods—including surrogate modelling, feature extraction, and inverse design algorithms Generate synthetic microstructures (based on the open-source OptiMic software) Perform descriptor
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Hannover Institut für Erdsystemwissenschaften / Abteilung Bodenkunde / Naturwissenschaftliche Fakultät Prof. Dr. Stephan Peth Address StreetHerrenhäuser Str. 2Zipcode30419CityHannover Contact details Tel:+49
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to advanced training opportunities for professional development About our group: Prof. Dr. Ziyue Li holds the Professorship in Transportation Analytics in the Department of Operations & Technology and Heilbronn
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addresses of two referees and be send by e-mail in a single PDF file until 15th of September 2025 with the job offer reference number ST-27087 to Prof. Johannes Brägelmann Medical Clinic III – Oncology
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Junior Research Group “ Probabilistic Methods for Dynamic Communication Networks“ (Head: Prof. Dr. B. Jahnel) starting as soon as possible. The position is within the Math+ project "Information Flow
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/ The underlying project is under supervision of Prof. Dr. Peter Zaspel and Prof. Dr. Michael Günther. The team of Prof. Peter Zaspel is located at Bergische Universität Wuppertal. The international team focuses
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in the Division of Proteomics of Stem Cells and cancer, headed by Prof. Jeroen Krijgsveld. Our division develops innovative proteomic approaches to understand proteome regulation in cancer, both in
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play a central role in this interdisciplinary initiative. They will: Develop and apply machine learning (ML) methods – including surrogate modeling, feature extraction, and inverse design algorithms