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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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applicant will be working on applying single molecule fluorescence microscopy and super-resolution microscopy techniques to study: i) the interactions between plasmonic metal nanoparticles and fluorescent
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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