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) and parallel computing, with a focus on cost-efficient and scalable model deployment. Skilled in working with medium-large scale multicore and heterogeneous (CPU + GPU) clusters. Excellent verbal and
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dedicated to research, advanced medical data streaming and processing machines, as well as state-of-the-art local and scalable cloud-based, compute infrastructure (CPU, GPU). Long-standing and very successful
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. This position offers substantial resources, including access to the SeaWulf computing cluster and cutting-edge GPU clusters housed at IACS and CEWIT. The Empire Innovation Professor will join a robust community
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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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structured and unstructured datasets, and GPU-accelerated computing. Proven experience with Large Language Models. Required Skill/Ability 3: Sound background in theoretical and applied machine learning/deep
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and GPU, as well as addressing relevant computational and algorithmic questions. Main duties: • To undertake research aimed at computational protein design by developing molecular simulation methods
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systems. Areas of interest may include, but are not limited to: - Advanced accelerator chip technologies, such as GPUs or other specialized chips for large-scale AI processing - High-speed memory
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familiarity with Deep Learning, including PyTorch would convey a significant advantage. They will have access to our in-house GPU-enabled High Performance Computing platform as well as national HPC resources
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concepts to project teams with varying levels of expertise. Serve as a liaison with Princeton Research Computing staff on GPU cluster-related issues. Application of Domain Expertise Collaborate with our
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provided with the working material (working station, PC and access to local GPU clusters) as well as with a discounted rate for the close-by Inria canteen, together with a gross salary of 2650 euros/month