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Field
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configuring GPU-enabled systems for deep learning tasks. o Network Administration: Strong understanding of networking concepts, including TCP/IP, routing protocols, VLANs, and Layer 2 switching. Experience with
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Processing Unit (GPU) hardware. Working Conditions Needs to be able to successfully perform all required duties. Office/research environment; some travel and weekend work is required. UTRGV is a distributed
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, and validated through simulation and experiments. For experiments, IDLab-UAntwerp's world-class research infrastructure, including GPU Lab, Software Defined Radios and programmable beamformers, as
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datasets, high-end GPU and storage infrastructure, international research collaborations, and dedicated funding for international conference participation. Supervision The PhD candidate will be supervised by
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modeling, multi-modal drug discovery, retrosynthesis, and AI for science more broadly. SPARC offers a collaborative, computationally rich environment with GPU/HPC infrastructure, large biomedical data assets
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dedicated GPU-equipped computing workstation and data-storage resources; - access to molecular-modeling and chemoinformatics software; - resources for the acquisition and experimental evaluation
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of environmental factors of 60,000 subjects across multiple time points. Our research laboratory has great computing capacity, including multiple H100 and A100 GPU systems for deep learning, and computing clusters
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, and maintaining automated data pipelines for large-scale time-series or imaging datasets. Experience with HPC/cluster computing environments, including SLURM job scheduling and GPU-accelerated
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, especially for GPUs across multiple hardware vendors, as well as experience in software sustainability and design patterns is expected. Major Duties/Responsibilities: Collaborate within a multi-disciplinary
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, e.g., git, automated testing, packaging, documentation and code review. Experience with GPU computing or high-performance computing. Interest or experience in LLMs, tool-using agents, or agentic