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24 Mar 2026 Job Information Organisation/Company Helmholtz-Zentrum Berlin für Materialien und Energie Research Field Physics Researcher Profile Recognised Researcher (R2) Established Researcher (R3
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2 Apr 2026 Job Information Organisation/Company Helmholtz-Zentrum Berlin für Materialien und Energie Research Field Physics Researcher Profile Recognised Researcher (R2) Established Researcher (R3
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Your Job: Synthesis and physicochemical characterization of energy materials or representative model systems Characterization of the dynamical and structural properties of energy materials with a
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landscapes, starting on 01.08.2026. The position is funded for 3 years. Remuneration is in accordance with the German public tariff scheme (TV-L Brandenburg), salary group E 13. This is a part-time position
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required. Access to such high-energy X-rays enables larger penetration depths and the investigation of materials under highly absorbing or extreme conditions. Achieving these capabilities is essential
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14 Mar 2026 Job Information Organisation/Company Helmholtz-Zentrum Berlin für Materialien und Energie Research Field Physics Researcher Profile Recognised Researcher (R2) Established Researcher (R3
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Gorlitz, Sachsen | Germany | 16 days ago
: 15.05.2026 Job description:Postdoctoral Researcher (f/m/d) in Theoretical Chemistry / Electronic Structure Methods With cutting-edge research in the fields of ENERGY, HEALTH and MATTER, around 1,500 employees
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discipline Experience with mammalian cell culture (including primary cells) is desirable Knowledge of pluripotent stem cell cultivation is desirable Experience with and high motivation for microscopy
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characterization (e.g. KPFM) of thin-film model surface for localized characterization across heterogeneous surfaces High-troughput data collection and physical parameter analysis for catalytic mechanism Exploration
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Germany | about 1 month ago
measurements. Implement physics-informed machine learning models to predict mechanical properties from cell morphology. Collaborate closely with experimental teams to integrate transcriptomic and imaging data