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Field
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://pritykinlab.princeton.edu) develops computational methods for design and analysis of high-throughput functional genomic assays and perturbations, with a focus on multi-modal single-cell, spatial and genome editing
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research and handling sensitive data is preferred. • Strong quantitative skills, including proficiency in regression modeling, environmental mixtures analysis, and spatial methods using R. • Familiarity with
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of biotic heterogeneity in urban environments and the spatial/temporal dynamics of these drivers. This will involve working with pre-existing data collected by the lab, as well as collaborator data and
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for improving the environmental impacts of wood production over broad spatial scales. Required Skills and Qualifications: PhD in a forestry- or conservation-related discipline. Strong background in field survey
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and Radiative Transfer models already developed as part of the ANR AGN_MELBa work. This might require running and adapting these models. Then, at a different spatial scale, the multi scale tools will be
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environmental sensor deployment, data analysis, and spatial mapping; Interest in nature-based solutions and working across disciplines; Excellent communication skills and the ability to collaborate with
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samples and run immunology assays including flow cytometry, single cell sequencing, spatial transcriptomics, and high throughput immune repertoire sequencing. Manage mouse colonies, including organizing
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wider Marine Spatial Planning (MSP) implementation, and exploring how Blue Carbon may be included in the EU and Ireland’s reporting requirements in response to climate change (Paris Agreement/NDCs). In
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esophageal tissue using a variety of mouse models, organoid co-cultures and spatial sequencing technologies to identify relevant cell-cell interactions. We seek a curious, highly motivated, and creative
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of neurons to become active during spatial navigation, in mice. To this aim, the candidate will investigate longitudinally - by using head-fixed two-photon imaging in combination with cellular labelling