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Leibniz Institut für Naturstoffforschung und Infektionsbiologie | Jena, Th ringen | Germany | 3 days ago
sciences Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 30 Apr 2026 - 00:00 (Europe/Berlin) Country Germany Type of Contract Temporary Job Status Part-time Hours
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, computer science, medicine, pharmacology, and physics. ISAS is a member of the Leibniz Association and is publicly funded by the Federal Republic of Germany and its federal states. In the department
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, computer science, medicine, pharmacology, and physics. ISAS is a member of the Leibniz Association and is publicly funded by the Federal Republic of Germany and its federal states. In the department
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, computer science, medicine, pharmacology, and physics. ISAS is a member of the Leibniz Association and is publicly funded by the Federal Republic of Germany and its federal states. In the department
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polymer science, supramolecular chemistry, and soft matter physics? The Research Training Group GRK 2516 – Structure Formation of Soft Matter at Interfaces is now recruiting 12 PhD candidates for its
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, United Kingdom Supervisor: Dr. Ivana Savic Focus: computational materials modelling Background: physics, computational materials science. or a closely related discipline Apply: https://mgician.eu/research/doctoral
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encouraged (also with partners in South America, Asia or Africa). The PhD process will be accompanied by integration into TUM’s School of Life Sciences or School of Management and participation in the related
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Max Planck Institute for Multidisciplinary Sciences | Gottingen, Niedersachsen | Germany | 8 days ago
Network program The selected PhD candidate will participate in the EU-funded HORIZON-MSCA-DN-2024-01 project LipAgg. The LipAgg network brings together partners from 6 European countries and comprises 11
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together 11 leading institutions across Europe to train 16 Doctoral Candidates at the forefront of theory and experiment in ultrafast science. The programme offers a unique, interdisciplinary environment and
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remains challenging, limiting multiscale imaging approaches in near-field holotomography. To address this, the PhD project combines machine learning, high-performance computing, and synchrotron-radiation