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or Biomedical Engineering or a closely related field. Preferred Qualifications: PhD in medical applications of physical sciences and engineering. Experience in medical imaging, breast x-ray imaging, image
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of electron microscopy and beam-material interactions. The ideal candidate will have a basic understanding of image processing. Core competencies: – Knowledge of image processing software (Avizo, Dragonfly, etc
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Eligibility criteria PhD in Applied Physics, Optics, Biomedical Imaging, or a related field; Strong expertise in experimental optics and optical instrumentation; Proficiency in image processing and data
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elucidating the molecular and cellular mechanisms of the late phase of long-term potentiation (LTP), a key process in learning and memory. The project is based on the development and use of an innovative
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Eligibility criteria - physics with knowledge in optics - knowledge in microscopy - knowledge in single-molecule imaging (acquisition and processing) - knowledge in photophysics - knowledge in cell culture
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working within Theme 3 of the Programme: ‘Image-guided Surgery’ and will involve the synthesis and assessment of novel multimodal nanomaterials for healthcare applications and radiolabel for PET imaging
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longitudinal consistency and data quality, including parameter tracking, protocol versioning, and rigorous QC frameworks Implementing, validating, and extending image-processing and analysis pipelines
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for optical imaging. Experience and interest in data analysis and the ability to perform basic biochemical work with proteins and DNA will be highly rated in the selection process. The ideal candidate will hold
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processing for wireless communication systems including satellite communications and radar systems and is currently expanding its research activities in exploring several emerging use cases of next generation
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into operational decision‑support tools for farmers, in close collaboration with an industry partner. The project focuses on automated rumen‑fill assessment using 3D imaging, computer vision, and predictive