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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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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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Oden Institute for Computational Engineering and Sciences | Austin, Texas | United States | 2 months ago
structure adjustment). Strong programming skills (e.g., Python, R) and experience working in Linux/HPC or cloud computing environments. Evidence of scholarly productivity (publications or substantial research
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experience in computational biology and its common applications (experience in R and/or Python, as well as cloud computing) A strong background in statistics and biology Experience managing and curating large
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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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-seq, ATAC-seq, ChIP-seq, spatial transcriptomics and single-cell data. Familiarity with data integration frameworks, predictive modeling, and network analysis. Experience with cloud computing
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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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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