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the scientific coordination and integration of the project's activities. Responsibilities include: • Data synthesis and integration across spatial and temporal scales, combining experimental, ecological
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methylome sequencing, custom NGS panels, liquid biopsy, and spatial transcriptomics. The primary focus of this position is to help optimize and complete research tasks related to the computational analysis
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experience of quantitative environmental data analysis, including climate data, ecological or forest inventory data, and/or spatial data sets, and skills in statistical analysis and data processing using tools
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of computational biology. have experience with data-intensive technologies, such as the analysis of spectral flow cytometry, imaging, single-cell transcriptomics, or spatial tissue profiling data, and should be keen
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of innovative methodologies for the analysis of synergistic multispectral satellite data and very high-resolution remote sensing imagery acquired from Unmanned Aerial Vehicles. The responsibilities include active
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. The position combines offshore fieldwork, laboratory analysis and modelling, with clear pathways to publication and impact. The outputs of this role will directly inform MOSAIC’s coordinated conservation
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(HPC) for biological simulations and analyses; multimodal data integration and analysis; new approach methodologies (NAMs). Precision Medicine and Biomedical Data: spatial transcriptomics and spatial
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developing approaches to leverage spatial data to better understand evolutionary histories. More information about the lab and their work can be found by visiting https://federlab.github.io/ About the
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Description Are you fascinated by how viruses overcome biological barriers to infect a cell? Do you have a strong interest in advanced microscopy, single-particle tracking or computational analysis? Are you
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. Your team You will collaborate with GRS colleagues who have expertise in methods and tools for spatiotemporal analysis of complex land systems (including agent-based modelling), spatial data