248 computational-physics "https:" "https:" "https:" "https:" "Dr" positions in Germany
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Offer Starting Date 1 Jun 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer
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as part of the application and selection process, please refer to the privacy policy on our homepage at https://www.senckenberg.de/en/imprint/ Please visit our website at www.senckenberg.de
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Project & Group: You will work in the Computational Chemistry group, led by Dr. Mehdi D. Davari, an interdisciplinary team focused on accelerating chemical and biological discovery using computational tools
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rerum naturalium, abbreviated to Dr rer nat) or the academic title PhD. The Department of Mathematics and Computer Science confers the academic degree of Dr rer nat. Alternatively, women can be awarded
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W3 Endowed Professorship for “Hemodynamic Modeling in Atherosclerosis- (f/m/d) KSB Foundation W3 end
are a completed university degree, a qualified doctorate (Ph.D., Dr. rer. nat., Dr.-Ing., or comparable) in a relevant field (e.g., computer science, applied mathematics, biomedical engineering, medical
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Degree Dr rer nat (PhD equivalent) in Astrophysics/Astronomy Course location Bonn In cooperation with University of Cologne Teaching language English Languages The entire programme is taught in
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Degree Dr rer pol Course location Frankfurt am Main Teaching language English Languages The doctoral programme at Frankfurt School is entirely in English. Full-time / part-time full-time Programme
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in cancers of unknown primary (CUP). Your Role You will join Subproject 3 (Model Alignment and Optimization), led by PD Dr. Keno Bressem (https://scholar.google.com/citations?user=wIEgwbkAAAAJ&hl=en
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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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, extending them with physics-based approaches, and adapting existing physics-integrated neural network approaches for stress prediction in arterial walls and plaque. Another part of the project is exploring