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results. Machine Learning skills to automise comparison process. Unbiased approach to different theoretical models. Experience in HPC system usage and parallel/distributed computing. Knowledge in GPU-based
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science education, computer vision, cybersecurity, data mining, high-performance computing, human factors in computing, Internet of Things, parallel and distributed computing, social computing, software engineering
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, computer graphics, computer science education, computer vision, cybersecurity, data mining, high-performance computing, human factors in computing, Internet of Things, parallel and distributed computing
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parallel processing, distributed computing, and resource management techniques for efficient resource utilization. Resource Allocation: Oversee the allocation of computational resources, ensuring scalability
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programming distributed systems; Experience with parallel and distributed File Systems (e.g., Lustre, GPFS, Ceph) development. Advanced experience with high-performance computing and/or large-scale data centers
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, theorem provers, type systems, categories, etc.), concurrent programming languages (choreographic programming, session types, etc.), distributed computing (cloud computing, microservices, etc.), Dev(Sec)Ops
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schools and the MD/PhD program of the CU School of Medicine. Our faculty and staff are diverse, and gender balanced, with women comprising 50% of our residents, Assistant, Associate and Full Professors