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machine learning frameworks (e.g., TensorFlow, PyTorch). Practical experience with cloud computing platforms (e.g., AWS, GCP, Azure). Additional Qualifications: Experience with multi-GPU model training and
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National Energy Technology Laboratory (NETL) | Pittsburgh, Pennsylvania | United States | about 10 hours ago
the early 2000s. Program Goals The Professional Internship Program is designed to introduce undergraduate students and recent Bachelor's graduates to the challenges of conducting energy research, and enable
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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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-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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: 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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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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biostatistics faculty collaborating with the Childhood Cancer Survivorship Program (CCSP). You will develop innovative biostatistical methods for childhood cancer survivorship research and collaborate with CCSP