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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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datasets. The Research Associate will: Design and implement high-performance workflows integrating GPU programming, deep learning, and large-scale data integration. Apply advanced methods such as ColabFold
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pipelines for large-scale biological datasets. The Research Associate will: Design and implement high-performance workflows integrating GPU programming, deep learning, and large-scale data integration. Apply
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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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learning frameworks such as PyTorch, JAX, or TensorFlow. Experience with C++ and GPU programming. A strong growth mindset, attention to scientific rigor, and the ability to thrive in an interdisciplinary
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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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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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communications. Evaluation of model performance can be conducted based on the data collected through the water tank. We have the GPU machines ($14k) to develop deep neural networks for underwater communications
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equipped with 8 GPUs. This infrastructure will enable the efficient training and evaluation of complex neural network models, essential for the project's success. The significance of this project for our