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funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Area of research: Scientific / postdoctoral posts Part-Time Suitability: The position is
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inviting applications for a PhD Student (f/m/x) for the project Theory and Algorithms for Structure Determination from Single Molecule X-Ray Scattering Images Project description Single molecule X-ray
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Sciences? The International PhD Programme (IPP) Mainz is offering talented scientists the chance to work on cutting edge research projects within the open call on “Molecular Biomedicine & Ageing”. As an IPP
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Your Job: Develop AI pipelines that translate -omic signatures into dynamic model parameters Implement reinforcement-learning agents that optimise model performance Collaborate closely with
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staff position within a Research Infrastructure? No Offer Description Work group: Interface Design Area of research: Promotion Job description: Reference No. SE 2025/23 The Department Interface Design is
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Karlsruher Institut für Technologie (KIT) | Karlsruhe, Baden W rttemberg | Germany | about 1 month ago
Job related to staff position within a Research Infrastructure? No Offer Description Area of research: Promotion Part-Time Suitability: The position is suitable for part-time employment. Starting date
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total of 17 doctoral candidates for project work lasting for 36 months. SPACER aims to develop new architectures for porous electrodes to improve the power density and energy efficiency of redox flow
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Your Job: The work is part of the joint project “Towards sustainable production by regenerative agriculture and agroecology in a changing climate” (ReGenFarm). Together with Bayer Crop Science, IBG
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Degree PhD, Dr rer pol Course location Jena Teaching language English Languages English Full-time / part-time full-time Programme duration 6 semesters Beginning Only for doctoral programmes: any
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Apply online now: https://karriere.klinikum.uni-heidelberg.de/index.php?ac=application&jobad_id=25541 PhD position - Functional and molecular characterization of AI-based morphological predictors