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Field
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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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compute hardware and network infrastructure for the ITS Research services. This covers the 12000+ core, 100+ GPU QM High Performance Compute cluster (see https://docs.hpc.qmul.ac.uk ), various hosted
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on GPU clusters and cloud HPC/ADK Implement data-efficient fine-tuning, adaptive learning workflows and agentic frameworks for reasoning Collaborate with machine learning experts and computational
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, immersive, and interactive technologies. Highly proficient in real-time engines, AI-assisted tools, GPU-accelerated platforms, and emerging computational design workflows, you combine creativity with
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AI pipelines (similar to the attached prototype). Enable users to configure model parameters, connect modules, and monitor training progress. Display performance metrics (e.g., inference time, GPU
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). Experience with distributed systems, GPU computing, or cloud-based simulation environments. Knowledge of human-in-the-loop simulation, training effectiveness evaluation, or synthetic environments. Experience
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: metrics, configs, checkpoints, weight versioning, model registry Simulation and Testing: Run large-scale cloud experiments; track throughput, GPU utilization, cost per run; evaluate robustness to preemption
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performance with energy consumption reduction. Key Responsibilities: Conduct research on energy-efficient computing systems, including CPU/GPU/accelerator architectures, virtualization, and workload scheduling
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, especially GPUs for AI calculations. Experience with research and/or practice in one or more STEM disciplines. Experience with collaborating with researchers (faculty, staff, postdoctoral researchers, graduate
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infrastructure, driving the design and evolution of HPC and AI platforms at scale. This role architects and implements next-generation GPU/CPU clusters, high-bandwidth InfiniBand and Ethernet fabrics, large-scale