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Field
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derived use cases by focusing on one or more of the following topics in their PhD project: Training and inference of ML models on GPU clusters. Method development for scalable and green AI. Use cases in
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in physics, mathematics or any related field; correspondingly, Postdocs hold a PhD or equivalent degree in the abovementioned fields. What we offer State of the art on-site high performance/GPU compute
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learning frameworks such as TensorFlow, or PyTorc. Experience with GPU programming and optimization for model training and inference. Familiarity with data preprocessing, feature engineering, and model
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of GPUs and/or time in either training or inference procedures, which pose considerable challenges to both academia and industry for widespread access and deployment. In particular, the sampling process of
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for early detection, differential diagnosis, progression monitoring, and treatment design. Key attractions are access to a high-performance computing cluster, NUS HPC (H100/H200 GPU clusters), two 3T Prisma
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conditions. Implementing a multimodal approach for large-scale data analysis using CPU and GPU Solutions at the UM6P Data Center. Innovate and improve image analysis algorithms for plant trait quantification
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, featuring 328 general nodes with 476 TB of RAM, and 448 GPU nodes with 31 TB of memory. We also have an AI/ML cluster, and an AI cluster, with over ~110 PB of storage for HPC computations. Applicants should
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has embraced the “infrastructure as code” approach to systems automation. You’ll be working across a range of predominately Linux based systems, including HPC and GPU accelerated compute, large-scale
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models including scaling models across a large set of GPUs; building or optimizing LLMs to tackle new, complex tasks; developing new models of brain circuits and function; and learning software engineering
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and a new GPU cluster. Opportunities for professional growth and career advancement. Collaborative and inclusive work environment that fosters creativity and innovation. Application of Domain Expertise