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BAM Bundesanstalt für Materialforschung und -prüfung | Berlin, Berlin | Germany | about 2 months ago
PhD student (m/f/d) in the field of mathematics, scientific computing, physics or an engineering discipline with a proven strong focus on numerical methods Berlin Division 8.4: Acoustic and
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weaker grade background, you maybe considered if you can document that you are particularly suitable for a PhD education. You must meet the requirements for admission to the faculty's Doctoral Programme
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Architecture, Planning and Management at the Swedish University of Agricultural Sciences (SLU) seeks a driven and committed PhD student with a focus on how governance structures, decision flows, departmental
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biogeochemical processes that govern their behaviour. Job description The PhD candidate will develop and analyse new process-based mathematical models to improve our understanding and predictive capability
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cycle (Academic Year 2026/2027) of the PhD Programme in Civil, Environmental and Mechanical Engineering at the University of Trento (Italy). Positions: no. 14 Scholarships: no. 14 Where to apply Website
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Applied Physics, Electrical Engineering, or the equivalent. The PhD position is firmly imbedded in a physics environment, but the candidate strongly interacts with partners in applied physics, applied
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guide how teams and organisations build quality software. As a PhD Candidate, you will carry out this work within the Software Engineering group at the Department of Computer Science, one of Norway's
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21 Sep 2026 Job Information Organisation/Company International PhD Programme (IPP) Mainz Department Institute of Molecular Biology Research Field Biological sciences Researcher Profile First Stage
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, you may be considered if you can document that you are particularly suitable for a PhD education. You must meet the requirements for admission to the PhD programme in Physics or Biophysics
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Are you interested in advanced computational modelling and state-of-the-art scientific software? Join us in creating the Bayesian multiverse: a computational framework for robust statistical analyses