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(further funding will be applied for) and focuses on power systems engineering in the context of the next phase of Iceland’s energy transition. It builds directly on established modeling work at Reykjavík
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Are you excited by ecosystem modeling, climate change and polar oceans? Do you enjoy quantitative work, coding, and turning ecology into equations? We are looking for a highly motivated PhD
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biology, spatial ‘omics, physiology), with innovative technologies and collaborative works on mammalian tissue and human organ transplantation. Harnessing these powerful approaches, our work spans multiple
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 18 days ago
regions where ground radar and gauge networks are sparse. In the reverse direction, high-resolution precipitation fields (e.g., MRMS) provide spatially distributed validation for fine-scale soil moisture
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PhD: Co-production of land use futures for Brazil Faculty: Faculty of Geosciences Department: Department of Human Geography and Spatial Planning Hours per week: 36 to 40 Application deadline: 13
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key research questions that crosscut the themes of statistics, graphical models, causal inference, active learning, spatial modeling, and deep generative models. Execute multiple projects that will
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population-based scientists and clinicians who conduct research for the prevention, diagnosis, and treatment of cancer. A central collaborative research model encourages translation of basic laboratory
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are fulfilled A full-time position (38.5 hours/week); part-time employment may be possible within the framework of certain working time models Access to state-of-the-art single-cell and spatial omics
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resistance. This work will integrate spatial transcriptomics, advanced co-culture organoid assays, and in vivo models to decode the mechanisms underlying CAF-driven CRC evolution. Access to single cell RNA
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multidisciplinary research projects. • Analyze, integrate, and interpret genomic, transcriptomic, proteomic, lipidomic, spatial multi-omics, imaging, and other high-dimensional biological datasets. • Identify