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Postdoc "Interferometric SAR Data Processing and Analysis for Implementation in the 3D-ABC Founda...
Area of research: Scientific / postdoctoral posts Job description: Postdoc "Interferometric SAR Data Processing and Analysis for Implementation in the 3D-ABC Foundation Model" (m/f/d) Background
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; significant practical experience in 3D image analysis or computer vision; knowledge and experience in scientific programming (python (preferred), Matlab or other relevant language) with application to image
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for a post doctoral researcher in computational design and digital fabrication. Reporting to Tsz Yan Ng, associate professor of architecture, you will work on robotic 3D printing processes such as
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approach that includes advanced bioimaging and image analysis, cell biology and genetics, and omics technologies. Qualifications We invite applications from candidates with a background in cell and
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, 2025, or as soon as possible thereafter. Project Overview This position is part of the LEO Foundation-funded research project titled “Exploring Autoimmune Skin Diseases with Immune-Integrated 3D Skin
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Supplemental Qualifications Background in neuroscience, developmental neuroscience/biology is preferred. Experience in 3D organoid development would be a positive. Candidate should be strong in molecular biology
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analysis and deep learning. You are proficient in implementing and optimising convolutional neural networks for medical image segmentation, with demonstrated experience working with 3D imaging data such as
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metallodrugs Mammalian cell culture including experience of 3D cell culture techniques. Confocal microscopy including quantitative image analysis Flow cytometry General molecular biology techniques such as
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microbeads form organoids or tumoroids, which are miniature 3D cellular models of biological tissues or tumors. Based on the microfluidic instruments previously developed in the team, you will design and
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students and staff is important. Prior experience in the following is considered a plus: 3D organoid cultures, any experience with in vivo mouse models, knowledge about analysis of omics datasets and