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21 Sep 2026 Job Information Organisation/Company Technische Universität Chemnitz Department Mechanical Engineering Research Field Technology » Production technology Researcher Profile First Stage
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Constructor Technology, invites applications for a PhD position in machine learning for software engineering and formal methods, on the Constructor Fabric project. Constructor Fabric turns a company's informal
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which technology and research fields are advancing. Furthermore, you will also extend the indicator beyond novelty toward quality assessment and validate the reliability, robustness, and fairness of LLM
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and accelerate breakthroughs in medicine, biology, and environmental health. Our interdisciplinary team of scientists and engineers from more than 25 countries develops technologies that bridge
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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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inaccessible biological information and accelerate breakthroughs in medicine, biology, and environmental health. Our interdisciplinary team of scientists and engineers from more than 25 countries develops
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Helmholtz-Zentrum Berlin für Materialien und Energie | Jena, Th ringen | Germany | about 2 months ago
of responsibility. You are characterized by your teamwork skills, reliability, independence, and communication abilities (including proficiency in English). You enjoy working in an interdisciplinary, international
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the quality of publications is to improve the reliability of literature-derived data in our technology database. Ensuring the quality of this database is essential because energy system models and their results
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, reliability, independence, and communication abilities (including proficiency in English). You enjoy working in an interdisciplinary, international team within a diverse and dynamic work environment. Further
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of heterogeneous operational data. However, limited availability, privacy constraints, and the variable quality of real-world datasets continue to hinder the development of reliable AI solutions for energy system