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Research Assistant (m/f/d) with a Ph.D. in Civil Engineering, Engineering Physics, Physics, Mathemat
less categories Discipline Applied Science , Engineering , Mathematics , Physics Job Type Research Assistant , Researcher Employment - Hours Full time Duration Fixed term Qualification PhD Sector
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<span lang="en">TUD Dresden University of Technology</span> | Dresden, Sachsen | Germany | about 18 hours ago
). The Horizon Europe project OptimAIse (Optimising Performance and Trust for Integrity-driven Modular genAI Software Engineering) addresses these challenges by delivering a scalable, modular, and
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fatigue loading. The models will be implemented and validated in commercial finite element (FE) software. The resulting FE model will define the digital twin of the welded structure and provide the basis
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-resolution drought prediction system. The topic of this PhD position is the monitoring and prediction of droughts on national scale. The PhD student will develop a novel model-based drought index
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our community. The School of Chemical Engineering ( CHEM School ) is one of the six schools of Aalto University. It combines natural sciences and engineering in a unique way. The Department of
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Mission We are offering a challenging position as a full-time (100%) PhD Student at STI IGM LAMD at EPFL, Neuchatel. You will be working on the TurboImpact project, funded by Horizon Europe – MSCA
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Luxembourg Location Luxembourg (Canton), Luxembourg Salary Negotiable Closing date 19 Oct 2026 View more categories View less categories Discipline Computing , Engineering Job Type PhD Position
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Scaling analog in-memory computing towards large generative AI models H/F Subject Your work will span device-to-algorithm co-design, focusing on state-of-the-art small language model engineering and
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mechanisms for information processing and learning, potentially inspiring novel paradigms that advance beyond traditional (silicon) computing. This PhD position aims to understand and advance in vitro neural
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Doctoral student in physics-guided foundation model for time-series data Reference number REF 2026-0434 Join us to develop physics-guided, data-driven foundation models for multivariate time-series