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Field
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Windows and Linux virtual machines for research and instructional purposes, including the allocation and optimization of GPU resources. Develop and maintain scripts and simple web applications to support
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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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facilities. Core responsibilities include the deployment and maintenance of small-scale HPC and compute nodes, GPU workstations, Linux and Windows servers, research data storage and backup solutions
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GPU acceleration (CUDA) Participation in relevant competitions (e.g., Kaggle, computer vision challenges) Experience with version control (Git) and collaborative development practices
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their publications Experience programming GPUs with CUDA, SYCL, HIP or OpenMP Experience using and developing code with AMReX Experience in performance engineering to improve code scalability and reduce time-to
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Implement sustainable and reproducible and FAIR research software engineering practices Collaborate with other HPC facilities and project partners Help evaluate and integrate GPU acceleration and other modern
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required) Experience with machine learning / deep learning (PyTorch; model training; GPU workflows). Experience with Transformers / text embeddings / multimodal modeling (e.g., Hugging Face ecosystem
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2026, UTC will deploy a state-of-the-art scientific computing and storage infrastructure, comprising a high-memory, GPU-enabled HPC computing node and a modular, scalable RAID-based storage system
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and writing scientific code - Knowledge of at least one of the parallel programming paradigms (MPI, OpenMP, GPU) - Proficiency in both spoken and written English is essential (work will be carried out
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to reinforcement learning, imitation learning, or learning-based control in simulation. Experience working with mobile manipulators, humanoid robots, or legged robots. Familiarity with GPU-accelerated simulation