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Field
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of code acceleration (GPU) Participate in numerical modelling (HPC (GPU), MPI Fortran / C, C++ Kokkos, Python, Perl) of SAMS front end and physics/test modules. Write research reports, progress reports
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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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GPUs). Research Associate: Hold a PhD in high performance computing, computational fluid dynamics or a closely related discipline*, or equivalent research, industrial or commercial experience. Research
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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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models, (d) experience in using high performance computing systems with multiple nodes and GPUs and (e) drought metrics. Familiarity with Texas water resources and management practices. Experience working
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implemented in the Fortran programming language, and it relies on the platform CUDA for parallelization of the computation over several GPUs’ cores, and has interfaces with Matlab and Python for ease of use
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or more GPUs; ability to work with pre-existing codebases and get a training run going Research interest in one or more of the following: Applied ML, Natural Language Processing, Computer Vision
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, computational algebra, logic and programming languages. The department is housed in the newly constructed Science & Innovation Center which boasts Data Center with High Performance GPU Cluster and state
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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