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to federal research proposals, sponsored research applications, and collaborative project reports. Familiarity with scalable computing environments, cloud platforms, high-performance computing, distributed AI
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role of land–atmosphere interactions in S2S predictability; impacts on boundary layer processes, aerosol-cloud interactions, precipitation, and hydrological extremes, including feedback mechanisms
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: Programming proficiency in at least R or Python (ideally both), plus comfortable use of Unix/Linux shell. Hands-on experience with high-performance computing (Slurm/PBS or equivalent) and/or cloud computing
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domains such as telecom, defence and cloud. You will join the Machine-Intelligence for Networks and Distributed Systems (MINDS) research group at the Department of Computing and Learning Systems, School
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-generation cloud chamber technology for exploring aerosol-cloud-precipitation interactions. The postdoc will lead and support cloud chamber experiments, operate a range of instrumentation, and work closely
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National Energy Technology Laboratory (NETL) | Pittsburgh, Pennsylvania | United States | about 23 hours ago
degree in computer science, information science, or another relevant STEM field. Previous experience with web development, machine learning, databases, and cloud platforms using Python and JavaScript
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at scientific edge systems using large-scale HPC/AI computational and storage systems. Design and evaluation of ephemeral, user-configurable, and composable data and storage systems. Evaluation of cloud data
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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maintain cloud-native genomic surveillance tools, datasets,and training resources. You will apply these to study insecticide resistance and the population structure of malaria mosquito vectors, generating
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biological data. · Proficiency in R and/or Python for data analysis and visualization. · Experience working with large datasets in an HPC or cloud computing environment. · Demonstrated ability to work