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. Our mission is to move beyond descriptive biology and develop predictive, mechanistic models that connect molecular regulation to cellular and systems-level phenotypes. The Laboratory of Computational
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. Process LiDAR data to support 3D analysis of terrain and settlements, integrating it with other spatial datasets to improve accuracy and create 3D models. Plan, execute, and process drone-based data
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ovarian cancer. The laboratory has about 15 members that use cutting-edge methods, including spatial proteomics, spatial metabolomics, spatial transcriptomics, 3D organotypic cultures of human tissue, in
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translational model systems. The research environment provides access to state-of-the-art facilities in genome editing, biological mass spectrometry, advanced imaging, spatial transcriptomics, and 3D
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lung fibrosis. The ideal candidate will independently perform studies utilizing established in vitro, ex vivo and in vivo preclinical models and will have the opportunity to develop and refine novel
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patients, canine patients, and mouse and organoid models -- offering a unique opportunity to identify conserved genomic features and accelerate biological insight. Duties include*: Prioritize and extract
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. Our mission is to move beyond descriptive biology and develop predictive, mechanistic models that connect molecular regulation to cellular and systems-level phenotypes. The Laboratory of Computational
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are of interest. The primary objective of this PhD project is to develop adaptive statistical models for marked spatial and spatio-temporal point processes. Many real-world systems exhibit substantial spatial
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nécessaire pour suivre les bilans des gaz à effet de serre, la production de biomasse et les rendements agricoles. À ce jour, la plupart des méthodes permettant d'estimer spatialement la GPP s'appuient soit
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the molecular signatures of proteostasis loss and identify early markers of proteostatic failure. The role combines wet-lab spatial biology with computational approaches. You will work across models and scales