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at New York University More Jobs from This Employer https://main.hercjobs.org/jobs/21805584/research-assistant-in-the-division-of-science-x5b-computer-science-x5d-dr-riyadh-baghdadi Return to Search
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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | about 2 months ago
, embeddings with transformers, training with flow matching) and high performance computing (e.g. handling large-scale parallel simulators, multi-node and GPU training on large supercomputers). When considering
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Deep Learning Computer Vision Edge AI, TinyML, and Embedded AI Explainable AI Safe AI Federated, Parallel & Distributed, Computing/Learning Control Systems Optimization Planning and Scheduling Human
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for scientific computing to support users in the process of solving increasingly complex compute- and data-intensive problems in science and engineering on high-end parallel and distributed computing platforms
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programming; Experience programming distributed systems; Experience with parallel and distributed File Systems (e.g., Lustre, GPFS, Ceph) development. Advanced experience with high-performance computing and/or
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management, program details, communication needs and other details to make events successful. Job Description Primary Duties & Responsibilities: Oversees, manages, and provides assistance as needed for special
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). Strong academic background and competencies in parallel programming, distributed computing, and performance engineering. Familiarity with accelerator programming (e.g, GPU), hardware programming, high
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) for a given Tiramisu program, many code optimizations should be applied. Optimizations include vectorization (using hardware vector instructions), parallelization (running loop iterations in parallel
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21 Nov 2025 Job Information Organisation/Company KTH Royal Institute of Technology Research Field Computer science » Programming Computer science » Other Engineering » Other Researcher Profile
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
and Experience: Distributed parallel training and parameter-efficient tuning. Familiarity with multi-modal foundation models, HITL techniques, and prompt engineering. Experience with LLM fine-tuning