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Field
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independence, good documentation habits and a track record of productive scientific results Preferred Qualifications Equipment Utilized High-performance computing; cloud computing; local workstations
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principles and research data management. Experience with high-performance computing, cloud computing, GPU acceleration, or distributed data processing. Experience participating in international scientific
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contribute to analyses and processing of data. They will become familiar with high performance computing environments and parallel processing, as well as cloud computing infrastructure. They will
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Health Initiative), and local Stanford data. Lab members have access to state-of-the-art computational resources, including cloud-based computing and Stanford high performance clusters. We offer
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schedulers (e.g., SLURM or qsub); experience with cloud computing platforms (e.g., AWS , GCP , Terra) is a plus · Experience developing and training deep learning models, ideally with fluency in
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molecular simulations is a plus. Experience with cloud computing and/or high-performance computing (HPC) resources. Application Process: Interested candidates should submit the following documents as a
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Postdoc position in Efficient NLP for Information Extraction from News Faculty: Faculty of Science Department: Department of Information and Computing Sciences Hours per week: 36 to 40 Application
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policy, agronomy, or a related field Desired Qualifications Strong skills in satellite remote sensing, GIS, and statistical analysis. Experience using cloud-based computing resources (e.g. Google
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computing platforms for data processing (e.g., AWS, Google Cloud) Understanding of BMS architecture and electric mobility systems. Share this job Facebook Twitter LinkedIn Apply now Apply for GTI
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using Snakemake, Nextflow, or similar platforms. • HPC or cloud computing environments. • Long-read or single-molecule sequencing, computational epigenomics, and/or 3D genome analysis. • Statistical