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Field
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understanding of data quality, reproducibility and robust analytical practice. Experience of SQL, cloud-based or high-performance computing environments, and Bayesian methods would also be valuable. Beyond
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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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computing, cloud-based computing environments, workflow-management systems, containers, and/or software development practices. Experience with machine learning, predictive modeling, or artificial intelligence
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(1) Post-Doctoral Research Grant with reference number BPD|2026/951 under the scope of the Project SHELL: Serverless High-density Environment for eLastic cLouds– refª LISBOA 2030-FEDER-00748300
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the McGill BioPortal. Develop and maintain reproducible analysis pipelines in R, Python, and shell on HPC / Slurm clusters and cloud environments (e.g., DNAnexus, Terra, AWS/GCP). Write first-author
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Resilience and Confidentiality in the Cloud), a EUR 2.5M collaborative project between KTH, Saab, Nvidia, Ericsson, Red Hat, CanaryBit and RISE, building next-generation secure and dependable AI for critical
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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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include: Conducting high-resolution model simulations of convective storms Contributing to model development using AI/ML approaches in areas such as clouds, turbulence, land–atmosphere interactions, and
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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