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Field
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of advanced statistical methodologies, and supporting research on high performance and cloud computing. The successful candidate will also be expected to offer 2-3 advanced technical or methodological workshops
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scalable bioinformatics pipelines on cloud-based infrastructure. The Research Fellow will be responsible for the code base supporting the large-scale genomic processing and analysis pipelines at the SMaHT
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, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and ICT Services & Applications. The Signal Processing and Communications (SigCom) research group of SnT, headed by Prof. Symeon
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discipline. Additional Qualifications Experience with running relevant numerical models (GCMs, mesoscale cloud-permitting or large eddy simulations) is desirable. Special Instructions Interested candidates
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academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and
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, spatiotemporal modeling, high-dimensional statistics. ● Proficiency in statistical programming (R and/or Python) and good practices for reproducible research. ● Experience working with large datasets and cloud
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with PyTorch, required to have experience developing code with a team through collaborative version control Experience working with large datasets and cloud computing environments. Solid background in
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research prototypes to real-world deployment environments, including cloud, secure enclaves, trusted research environments, and leadership computing platforms. Candidates should be comfortable working in a
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analytics projects as needed (e.g., ticketing, fan engagement, and scheduling). Build and maintain data pipelines and models using R, Python, SQL, and cloud computing resources, following best practices
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computing and/or cloud computing; familiarity with Earth system models through model development, model execution, and/or model performance diagnoses; applied mathematics methods such as machine learning