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. The project follows a bedside-to-bench-and-back approach, utilizing human tissue single-cell and spatial multi-omics at disease remission (inflammation resolution), human tissue organoids, and the use and
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the localisation of inflammation in the joints. The project is based on a bed-to-bench-and-back approach , utilising human tissue single-cell and spatial multi-omics , engineered CAR-T cells,human organoids , and
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of employment: 1-year fixed-term contract (CDD), renewable for 4 years. Informations Only applications submitted through the online platform are considered: https://careers.epfl.ch/job/Lausanne-PhD-positions
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modeling, iPSC disease modelling, cell sorting, live cell imaging, spatial and single cell omics modalities, and advanced image processing and analysis, amongst a variety of other specific departmental
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for Spatial Biology Description: The Lu Lab at Stanford University is seeking a postdoctoral fellow with deep expertise in advanced AI and generative modeling to develop computational frameworks that transform
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examines the collective impact of these changes on the livelihoods of Indigenous communities in the Yukon Territory, Canada. The RESILIENCE project aims to study how spatial patterns within tundra ecosystems
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on Indigenous Knowledge Systems. https://abundant-intelligences.net/ . The Toronto pod is comprised of faculty from OCAD University and York University in close partnership with collaborating institutions
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with EDF UK. The project offers the chance to develop skills in agent based modelling spatial-analysis, optimisation and economic analysis, as well as contribute to the UK’s decarbonisation. How to apply
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 hours ago
developing software and analytic tools to perform disease modeling and critical analytics in response to infectious disease outbreaks. ACCIDDA is part of the newly formed CDC-funded national network of centers
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translational value. Dr. Hopp’s lab utilizes state of the art models and technologies which include multiomic approaches (scRNAseq, spatial RNAseq, spectral flow cytometry, metabolomics etc.) to understand each