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nearby highways, an indoor SDR testbed for research on next generation wireless communications and sensing, and a GPU Lab for training of advanced machine learning models. IDLab is both part of
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systems, they increasingly reach their thermal limits due to rapidly rising power densities in modern CPUs and GPUs. Liquid cooling technologies, such as Direct-to-Chip (D2C) can dissipate higher heat loads
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limited to deep learning Experience utilising GPU enabled High-Performance Computing environments is an asset Open minded critical thinker, willing to actively contribute to the further development of multi
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development skills Model deployment (e.g., ONNX, TensorRT) Edge computing or embedded vision systems (e.g., NVIDIA Jetson Nano) Real-time processing and GPU acceleration Experience working on industry R&D
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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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, OpenCV), GPU computing (e.g., CUDA), SLAM and point cloud processing (e.g., PCL). Familiarity with ROS-based development and Linux software tools is considered a fundamental asset. (30
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of this, Tiramisu can generate fast code that outperforms highly optimized code written by expert programmers and can target different hardware architectures (multicore, GPUs, FPGAs, and distributed machines). In
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managing experiments using GPUs Ability to visualize experimental results and learning curves Effective inter-personal and team-building skills Self-motivated with an ability to work independently and in a
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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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tracking), dataset curation, HPC/GPU programming, blockchain for secure data, C-family languages, and embodied AI/robotics are a plus. Experience with general network resilience, cellular automata