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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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of Biomedical Engineering (SBME). The successful candidate will work on advanced machine learning applications in computational pathology, medical imaging, and clinical text analysis. They will be expected
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astrophysics previously! Candidates should hold a Master’s degree in Physics or Astrophysics, Optics, or computer science, with an interest in instrumentation, experimental work or signal processing. Experience
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cooperation partners in their research projects with state-of-the-art light and electron microscopes (https://www.leibniz-fmp.de/cellular-imaging ). We work with proteins, cell cultures, and model organisms
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Engineering, Biomechanics, Computer Science, or related field Preferred Experience: Experience with machine learning in medical imaging/biomechanics; grant writing support; clinical gait analysis in clinical
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imaging data, computer models, and statistical tools. The individual will prepare manuscripts and contribute to the development of grant proposals. The McIntyreLab currently conducts research in the areas
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, genetics, and neurodevelopment, along with access to state-of-the-art imaging technology and advanced computational resources. The fellow will have structured opportunities for professional development
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9 Feb 2026 Job Information Organisation/Company CNRS Department Sciences et Ingénierie, Matériaux, Procédés Research Field Computer science Mathematics » Algorithms Researcher Profile First Stage
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Fundación para la Investigación Biomédica del Hospital Gregorio Marañón (FIBHGM) | Spain | about 2 hours ago
funded through the EU Research Framework Programme? Other EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Degree in Biomedical Engineering or
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Imaging IT team architects, implements, and manages the computer systems and networks needed to support more than 700,000 imaging studies annually. Our team manages the full stack from network