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at international conferences. Development and optimization of experimental protocols: stereotactic injections, optogenetics, chemogenetics, in vivo imaging (fiber photometry, calcium imaging). • Development and
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accreditation • Proven experience working with animal models • Extensive expertise in histological and imaging techniques, including confocal microscopy • Proficiency in image analysis software (e.g., ImageJ
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(currently at Southampton University) and with data processing experts in the MATISSE international consortium. Her/His first main task will be to combine the existing image reconstruction codes
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BioNanoImaging Foundry (BNIF, Building 50, Triolet campus). The BNIF is a cutting-edge technological environment dedicated to multimodal imaging, equipped with high-field MRI scanners (9.4 T, 3 T horizontal
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You will develop and validate advanced AI models that integrate medical imaging, multi-modal clinical and omics data, and explainable AI (XAI) for the prediction of hepatocellular carcinoma (HCC
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Post-Doctoral Position - Medical Image Processing - (240000Y9) Post-Doctoral Fellow Position in Medical Image Processing (Deep Learning for Trauma CT) The Trauma Radiology AI Lab (TRAIL) in
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: Qualifications required: The candidate must have a PhD's degree in Biophysics, Biology, Bioengineering or equivalent. Expertise in image/data analysis and coding is required (Python, Image J). Experience in cell
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period of 12 months in the first instance, with potential to extend. The project involves advanced theory for new ultrafast imaging experiments and is funded by Engineering and Physical Sciences Research
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experience in the document image analysis field. The applicants are expected to be fluent in both oral and written communication in English. They should work well in a team while demonstrating initiative and
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, embedded controllers, and closed-loop control routines on prototype hardware; preparing technical reports, delivering presentations to stakeholders, and contributing to journal and conference publications