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therapeutic strategies to promote these beneficial immune states. Position Overview The Postdoctoral Fellow will lead multi-omic data generation, integration, and analysis to dissect immune–tumour interactions
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) microscopy, FLIM, STED, lattice lightsheet microscopy, fluorescence correlation spectroscopy (FCS), image processing and analysis and spectral imaging. More information about the ABIF can be found at: http
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link these to mechanical properties. To be done in partnership with Hydro-Québec. The potdoc will develop both advanced computing and numerical analysis skills, as well as acquire new knowledge in
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analysis programs, specifically GraphPad Prism or R, will be a benefit. The candidate will have an MSc and prior pain research experience. They will have expertise with relevant experimental techniques
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generation, integration, and analysis to dissect immune–tumour interactions in pediatric gliomas. The successful candidate will work closely with clinical, experimental, and computational collaborators across
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, such as a familiarity with data analysis software (e.g., Python, IDL, MATLAB, FORTRAN) Prototype algorithms, validate outputs, and document methods clearly Qualified and interested applications are invited
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. Qualifications: PhD in machine learning, with experience in applications in computer vision or medical image analysis. Strong publication record in top venues (e.g., CVPR, MIDL, MICCAI, IPMI, PAMI, TMI, MIA