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learning, deep learning, and large language models (LLMs), for the analysis of high-throughput multi-omics datasets (especially single-cell and spatial omics) and large textual corpora (e.g., scientific
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detection framework for tipping points. Contribute to the design of scalable and interpretable forecasting strategies for large climate simulators, integrating adaptive sampling and Bayesian techniques
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software delivery f) big data visualization g) cloud computing h) web application development i) NetCDF and Zarr data models f) earth system model development. Experience with software development, analysis
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of innovative mass spectrometry-based workflows and data evaluation tools. The developed metabolomic and exposomic tools will be applied to address pressing research questions at the edge of environmental/food
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mechanisms underlying risk for addictive disorders—with a primary focus on tobacco dependence and relapse—and translating this information to develop more efficacious interventions. This position is supported
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candidate will be working with a large database comprised of prairie and marsh vegetation observations, fine-scale LiDAR data-derived ground elevations, Everglades-wide hydrological modeling and observations
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test planning, instrumentation (e.g., strain gauges, LVDTs, DIC), execution of large-scale tests, and data analysis. Solid understanding of structural behavior, failure mechanisms, and durability issues
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us to explore the relation between the degree and type of processing, and the foods that result from their use. The results will be used for data machine learning, in collaboration with other partners
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. The successful candidate will contribute to advancing climate risk assessment, integrating scientific and local knowledge, and utilising innovative sensor data and community-generated observations to improve
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medicine in psychiatry, building on advanced statistical methods for analysis of large-scale human genetic and neuroimaging data, to better understand how biological, psychological, and environmental factors