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reconstruction, processing, synthesis, and registration, as well as AI for treatment outcome prediction and clinical decision making. The projects will involve using multi-modality images (CT, CBCT, MRI, PET
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 22 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