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Field
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Fellow of AAPM. Details about Dr. Ren’s profile can be found at the following link: https://www.medschool.umaryland.edu/profiles/Ren-Lei/ Equipment includes a computer cluster with high-end GPUs, and state
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. The researcher(s) will be provided access to state-of-the-art supercomputing facilities with advanced GPU and data storage capabilities. Additionally, opportunities will be available for collaborations. Duties
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NVIDIA GPU nodes. Additionally, The CSM maintains enterprise computing infrastructure consisting of 20+ servers/devices. This includes various storage devices (TrueNAS, Synology), web servers and Windows
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workflows, FAISS/embedding retrieval, LLM-based parsing, RAG-style pipeline, and GPU/HPC training. Familiarity with 3D data processing or willingness to learn quickly. Publications, thesis work
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commonly used on Unix systems. Additional languages or experience with libraries for utilizing GPU hardware efficiently, e.g., CUDA, are a plus. Experience in AI programming with, e.g., PyTorch(-DDP
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Storage Scale) HPC environments and job scheduling systems AI workloads and GPU infrastructure Containers and orchestration (Docker, Kubernetes) Infrastructure as Code and DevOps practices What We Offer We
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environment with access to domain expertise ranging from cyber-security over AI, electrical engineering, to clinical practice Extensive resources to support your work (e.g., CAD tools, GPU servers, wet lab
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resources to support your work (e.g., CAD tools, GPU servers, wet lab space) Research with impact – we are directly addressing the needs of multiple local stakeholders which are representative of the global
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: Experience in real-time simulation hardware like Opal-RT and RTDS. Experience with software development. Experience with use of DSPs, FPGAs, advanced computing (e.g., GPUs, QPUs). Excellent written and oral
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part of the core PLI team, which includes top-tier faculty, research fellows, scientists, software engineers, postdocs, and graduate students. Fellows will have access to the AI Lab GPU cluster (300