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work-life balance. At the division of Applied Mathematics and Statistics we conduct research within the theory and implementation of biomathematics, biostatistics, spatial modeling, differential
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, infectious disease modelling, mutational signatures in cancer, -omics data applications, spatial and temporal analyses, pharmacoepidemiology, quasi-experimental methods, implementation science, generative
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-range of research activities which encompass laboratory, field and modelling studies related to atmospheric chemistry, air quality and climate. This research project is called Partnership for Evidence and
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for the Anthropocene. Global Ecol Biogeogr 27, 760–786. https://doi.org/10.1111/geb.12729 Hughes, S., Kunin, W., Ziv, G., & Watts, K. (2023). Spatial targeting of woodland creation can reduce the colonisation credit
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is an award winning, multi-sectoral team of researchers, technicians and project specialists working on producing data on population distributions and characteristics at high spatial resolution. We
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Results: Ability to assess the influence of demise process on the release of emissions into different atmospheric layers. Numerical model to describe the temporal and spatial dispersion behaviour of ablated
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biomolecular chemistry techniques. Evaluation of biological behavior in cell models through cell culture, flow cytometry, and biocompatibility assays within the framework of the ERC-STG SPACING (SPAtially
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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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-disciplinary, place-based, partner-connected, justice-centered, and action-oriented. Job Description Research Associate: Computational Models of Coupled Natural and Human Systems APPLICATION INSTRUCTIONS
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Pest Management in The Western U.S.' (https://ai4sa.ucr.edu/ ). The overall goal of this project is to develop advanced tools for early stress (abiotic and biotic) detection and decision support for crop