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, implementing efficient monitoring of the various deployments using Grafana and Prometheus and the autoscaling of compute nodes for CPU and GPU workloads across various cloud providers. Key responsibilities will
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configuration and management Knowledge of Linux and GPU scripting, storage management, quantum computing, and cloud systems Here's how to apply: Please submit your updated resume and a short cover letter
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for GPU-accelerated applications. Data Engineering Tools: Proficiency in data engineering tools, including Apache Airflow for workflow orchestration, and message brokers like RabbitMQ or Kafka
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. Demonstrated history in Astronomy research or engineering. Experience in Interferometric Imaging and Calibration. Experience with Python package development and deployment. Experience with GPU application
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infrastructures, improving system performance, scalability, and efficiency by optimizing resource usage (e.g., GPUs, CPUs, energy consumption). Researchers and students will explore innovative approaches to reduce
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containerisation (e.g., Docker) and orchestration tools (e.g., Kubernetes) for deploying and managing applications at scale, including support for GPU-accelerated applications. Data Engineering Tools: Proficiency in