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for improved interpretability and generalization. Familiarity with high-performance computing (HPC), cloud platforms, or GPU clusters. Demonstrated ability to work collaboratively in interdisciplinary and cross
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metrics, or similar frameworks. Strong programming skills in R, Python, or similar, with the ability to write reproducible, well-documented code. Familiarity with high-performance computing (HPC
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and Machine Learning tools and algorithms to solve hydrology and water resources problems. Familiarity with high-performance computing (HPC), cloud platforms, or GPU clusters. Demonstrated ability
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network data structures and their manipulation Demonstrated expertise in the application of HPC resources for scientific computing Experience with Graph and text embedding tools Research experience using
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algorithms to solve hydrology and water resources problems. Familiarity with high-performance computing (HPC), cloud platforms, or GPU clusters. Demonstrated ability to work collaboratively with
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computer science and high-performance computing (HPC) research. About Computing Sciences at Berkeley Lab: Whether running extreme-scale simulations on a supercomputer or applying machine-learning or data analysis
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outstanding contributions in computer science and high-performance computing (HPC) research. About Computing Sciences at Berkeley Lab: Whether running extreme-scale simulations on a supercomputer or applying
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the computational models using benchmarks and experimental results while assessing and improving the performance of algorithms on high-performance computing (HPC) clusters. This is a fantastic opportunity for you to
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experience in machine and/or deep learning applied to geospatial data. Demonstrated experience in the use of HPC and handling of very large datasets. Experience in software development and contribution
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cancer genomics and functional interpretation of genetic variants Proficiency in Python, R, or other bioinformatics languages Knowledge of cloud computing, and high-performance computing (HPC) environments