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partitioning for parallel/distributed AI/ML Optimization of process-to-process communication in parallel/distributed AI/ML Enhancement of AI/ML with in-network computing & processing Adaptation & optimization
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22 Jan 2026 Job Information Organisation/Company SINGAPORE INSTITUTE OF TECHNOLOGY (SIT) Research Field Computer science Engineering Researcher Profile Recognised Researcher (R2) First Stage
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engineering; Formal methods, models, and languages; Interactive and cognitive systems; Distributed systems, parallel computing, and networks. The successful candidate will work closely with teams specializing
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, nuclear and biomedical fusion through the experimental laboratories of rapid prototyping, biomedical and photonics. In the field of High Performance Computing (HPC), activities related to parallel
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through sensitivity, uncertainty, and scalability analyses. – Enhance the computational efficiency of large-scale optimization problems by exploring decomposition techniques, parallelization, and
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, or deployment at scale. A proven track record of high-quality research contributions published in top-tier machine learning conferences or journals. Proficiency in high-performance computing, distributed and
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hardware architectures (multicore, GPUs, FPGAs, and distributed machines). In order to have the best performance (fastest execution) for a given Tiramisu program, many code optimizations should be applied
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(fastest execution) for a given Tiramisu program, many code optimizations should be applied. Optimizations include vectorization (using hardware vector instructions), parallelization (running loop iterations
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linear algebra computations, building software for scientific applications using GPUs (Graphics Processing Unit), multi-threading and parallelism, numerical discretization methods (finite differences
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 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