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or scripting in Python, R, or bash/zsh on Linux/HPC environments. Experience working with clinical research data, including neuroimaging and clinical data collection, image processing, and statistical analysis
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co-culture systems Have prior experience in molecular biology techniques, such as RNA sequencing, Western blotting, qPCR, immunocytochemistry, or live-cell imaging Show an aptitude for designing and
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analytical chemistry and mass spectrometry. The project involves developing characterization methods using mass spectrometry (FT-ICR, TOF-SIMS) and imaging techniques (SEM, TEM) for both biological and
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vision transformer or large vision AI model Expertise in high performance computing Expertise in image and spatiotemporal data processing Expertise in federated learning on large computing clusters A
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microtubule nucleation is regulated in the context of neuronal development within Drosophila. The recruited postdoctoral fellow will use powerful genetics and advanced cell imaging of neurons in situ
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biomedical image processing. Within the scope of machine learning and computer vision, there will be freedom to suggest your own research directions, and to become acquainted with new techniques and approaches
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engineering, computer engineering and applied physics, with a solid academic background in biomedical imaging and AI and an excellent research track record. Candidates should demonstrate a strong academic track
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volunteers and one in patient populations. Be involved in all aspects of the research: recruitment, data collection, management, public and patient involvement, imaging processing, and analysis. Liaise with
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imaging in molecular systems, as demonstrated by scientific publications in the field. Required skills include operation and alignment of laser instrumentation, and programming experience in e.g. MATLAB
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myeloma, as well as to understanding immune aging processes, through comprehensive functional, phenotypic, and genetic analyses of large, dedicated patient biobanks. To elevate our research to the next