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. Assessing the risk of congenital malformations, fetal loss, child mortality and developmental issues due to ZIKV infection is challenging due to data scarcity, lack of appropriate ZIKV-exposure ascertainment
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are: Create favourable circumstances for the development of a field of research and a community of research on access to justice for OLMCs Produce scientific data for and with the access to justice stakeholders
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evidence from diverse sources, analyze international survey data, and contribute to the development of a conceptual framework that captures the multi-level and intersectional nature of stigma. Findings from
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tissue collection infrastructure, generate foundational single-cell and spatial omics datasets, and develop patient-tissue glioma organoid (PTGO) models to test immunotherapy strategies. The ultimate goal
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as an exceptional researcher in the field of developmental trauma. Her research addresses a pressing social issue: the intergenerational (dis)continuity of child maltreatment. Her innovative approach
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: Department of Physics Position Summary: Development of a comprehensive phase field modelling platform for the quantitative examination of microstructure evolution and properties in two-phase Ferritie Steel and
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in Prof. Reza Salavati's lab, at Macdonald Campus, McGill University. Position Summary: The postdoc will contribute to the D2R (Data-to-Reality) applied research program aimed at developing novel data
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://www.mcgill.ca/abif The primary duties will include: Advanced Microscopy: Work with a collaborative team to develop resources and protocols for sample preparation, live cell lattice lightsheet and STED imaging
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organoid-based approaches. Together, the teams will establish a bi-national tissue collection infrastructure, generate foundational single-cell and spatial omics datasets, and develop patient-tissue glioma
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Research, and Meta. Responsibilities: The Postdoctoral Fellows will be responsible for leading ongoing innovative research projects. Examples include: The development of probabilistic deep learning models