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the European Commission. For any inquiries, do not hesitate to contact us via mariecuriemasterclass(at)dkfz.de . Where to apply Website https://www.dkfz.de/en/career/cancer-research-academy/international-postdoc
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 4 hours ago
the nation for federal research expenditures as well as for federally funded social and behavioral sciences research and development. Here at Carolina, our highly skilled postdocs play a vital role in our
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Area (EEA) in accordance with the GDPR (the provider's declaration in a continuously updated version can be found at https://cloud.google.com/terms/data-processing-addendum/ (Appendix 3: Specific
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our interdisciplinary team. We study the chemical and biophysical principles underlying membrane-involved processes in neurodegeneration and cancer, with emphasis on tauopathies, membrane repair, and
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23 Mar 2026 Job Information Organisation/Company Universidade de Coimbra Research Field Other Researcher Profile Recognised Researcher (R2) Positions Postdoc Positions Application Deadline 7 Apr
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), Computational Biology (stochastic and analytical models of gene expression), Signal Processing (machine learning, image and signal processing), Biophysics, Microbiology and Single-cell Biology (flow cytometry
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have strong programming skills in Python; You have knowledge of medical image processing, and machine learning and deep learning techniques; Written and spoken proficiency in (scientific) English is
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numerical results with observations from scanning and transmission electron microscopy provided by the partners of the ANR project IMP3D (https://anr.fr/Projet-ANR-24-CE08-3737 . - Select a discrete
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& Sciences (SCMMS) provides an outstanding multi-disciplinary environment for the pursuit of cutting-edge cardiovascular and metabolic research (https://www.kcl.ac.uk/scms ). We study the fundamental molecular
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Germany | 14 days 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