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gut hormone receptor drug discovery programme. The successful candidate will help lead on in vitro pharmacology and in vivo testing and will also be involved in peptide ligand design process alongside
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interdisciplinary radiology and mammary surgery sections for efficient patient pathways. A dedicated mammary coordinator organises cancer screening processes. The role involves imaging diagnostics (clinical
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looking for a PhD Student (f/m/d) to work on the Development and Evaluation of PET Image Reconstruction Methods for Simultaneous Clinical Dual-Tracer PET Imaging. Positron emission tomography (PET) is a
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motivated Preferred Qualifications: Experience with serial section EM Histology Viral and immuno labeling Stereotaxic surgery Image analysis software and systems (e.g. FIJI/ImageJ, EyeWire) Light microscopy
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– Resolution of appeals 16.01.2026 – 4:00 PM – Final results Additional comments We are seeking a PhD student in Machine and Deep Learning for Satellite Image Processing to join our team, who will be
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to imaging with geophysical data. Our research focuses on mathematical methods for processing, imaging, and inversion of geophysical data, the physics of wave propagation, and the development of innovative
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. The position concerns research in the above-mentioned projects. Requirements PhD degree in Medical Image Processing or a foreign degree equivalent to a PhD degree in Medical Image Processing. The degree needs
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month postdoctoral contract to work in a multidisciplinary research project that combines photonics, neuroscience, molecular biology, in vitro and in vivo calcium imaging. The hosting group is led by Dr
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-of-the-art infrastructure and facilities, such as the Life Science Imaging Centre (https://research.hkbu.edu.hk/resources/facilities-and-labs/hkbu-life-science-imaging-centre). Being part of a rapidly evolving
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. Stefanos Zafeiriou can be found at https://wp.doc.ic.ac.uk/szafeiri/ . Research Associate: A PhD (or be close to completion) in an area pertinent to the subject area, i.e. computer vision, machine learning