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Field
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cancer genomics resources and databases, including The Cancer Genome Atlas, cBioPortal, Genomic Data Commons, dbGaP, GEO, and related resources. Experience with high-performance computing, cloud-based
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. Experience working with satellite remote-sensing data such as Landsat, Sentinel, MODIS, SAR, LiDAR, or derived land-cover and vegetation product, and experience with in-the-cloud image processing Experience
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engineering or similar. You have an excellent English proficiency (C1), both in speaking and writing. Knowledge of Dutch is an asset. Core technologies you are familiar with: BIM, point clouds You are capable
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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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(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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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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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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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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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