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vivo models, immune–metabolic signaling, flow cytometry, and cell death or redox biology. Experience with single-cell or spatial transcriptomics is highly desirable. Independent yet collaborative
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leverage large-scale AI models to: integrate heterogeneous EO data sources, such as satellite, aerial and in-situ data, across spatial and temporal scales; enable zero-shot or few-shot learning for rapid
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, epidemiological, and environmental data Taking part in developing and validating predictive cancer‑risk models Contributing to spatial analysis and data integration in geographic information systems (GIS
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Transformation (GALLANT): https://www.gla.ac.uk/research/az/sustainablesolutions/ourprojects/gallant/ . This post will be based in Work Package 2 ‘Biodiversity and societal benefits of ‘natural’ urban habitats
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the social and environmental implications of proximity models, combining spatial, mobility and socioeconomic data to develop indicators and tools for the toolkit. The candidate will also assume coordination
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Position as Computational Analyst / Bioinformatician in RNA Therapeutics and Cardiometabolic Disease
intersection of (micro)RNA biology, 3D human model systems, nanomedicine, and computational (ML) disease modelling. You will be able to contribute to our vision to translate basic findings into medicinal RNA
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the intersection of mucosal immunology, cancer, and spatial transcriptomics, with the ultimate goal to understand and harness immune responses in human diseases. The Engblom and Villablanca labs form a tight-knit
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of innovative research methodologies and will independently design, optimize, and execute complex immunologic and molecular experiments across in vitro, ex vivo, and in vivo model systems. Technical scope
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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | 28 days ago
the A20 mouse lymphoma syngeneic model with anti-mouse PSGL-1. The lymphoma microenvironment spatial composition and immune cell activation states will be characterized by advanced techniques. We also plan
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on expectations elicited via tailored household and firm surveys (carried out by another team member) and other spatial and physical climate risk data. The goal of this agent-based modeling is to identify