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Field
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-resolution imaging approaches Analysing data, publishing findings and presenting research to academic and project audiences What will you bring to the role? A PhD, or near completion, in a relevant scientific
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data and deep learning methods to assess canopy cover, quality, carbon stocks, and ecosystem services. Mandatory requirements: PhD in areas related to forest resources, remote sensing, data science, or
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(MRO), as well as interpretation of results and collaboration with CEMol teams and partner institutions. Mandatory requirements: PhD in Materials Science or related fields; proven experience in
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quantitative microscopy. Ideal Candidate Qualifications: Candidates must have recently obtained (or will obtain before starting) a PhD from a biomedical research background. Experience in one or more areas of
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• Perform experiments to evaluate the biodistribution and efficacy/safety of nanoparticle formulations. • Interpret data with advanced methods such as flow cytometry, confocal microscopy, MRI/PET imaging
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technical problem solving. Work collaboratively with the CI, PIs, research staff, PhD candidates, Honours students and external project partners. Assist the CI and PIs with the technical supervision and
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using Machine Learning who has: strong experience in signal processing, machine learning or a related field a PhD (or near completion) in signal processing, machine learning or a related field experience
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collaborative research in electromagnetic sensor systems, including theoretical analysis, numerical modeling, AI-assisted sensing algorithm development, and experimental validation Qualifications: An earned PhD
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collaborative research in electromagnetic sensor systems, including theoretical analysis, numerical modeling, AI-assisted sensing algorithm development, and experimental validation Qualifications: An earned PhD
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histology, immunohistochemistry, in situ hybridization, organoid/tissue explant cultures, and live imaging. Analyze complex data, including bulk, single-cell, and spatial transcriptomics datasets. Present