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implementing novel and innovative tools, technologies and approaches to fundamental problems in systems and circuit-level neuroscience. For more information about the lab check out: https
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information about the lab check out: https://www.moorelabstanford.com/ . About the role: The role will be in-person with hybrid flexibility and is a perfect opportunity for someone looking for a 1-year, fixed
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with edge computing or embedded systems (e.g., NVIDIA Jetson, Raspberry Pi) Background in real-time processing and GPU acceleration (CUDA) Participation in relevant competitions (e.g., Kaggle, computer
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and oral communication skills good ability to cooperate good knowledge of English, spoken and written C/C++ and GPU programming experience (CUDA, DX, Vulkan) Other qualifications For the doctoral
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, resource requests, and environment management. Desired Requirements: 1. Probabilistic modeling: scVI/scANVI/totalVI for RNA and RNA+protein integration. 2. GPU experience: PyTorch/CUDA for segmentation/model
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development tasks will be helpful as well. This includes knowledge of one or more of the following: PyTorch, CUDA, MCP (or any other AI/ML tools) as well as Docker, Kubernetes, PHP, Web Services, Database
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Proven experience with multiple parallel programming paradigms, including but not limited to; MPI, OpenMP, and CUDA (Compute Unified Device Architecture) Experience in a batch HPC environment with a
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MKL, Intel oneAPI, Matlab, CUDA, and real-time audio processing. • Experience with C++, Java, and Python. *Incumbent will be required to submit for a Department of Defense Secret-level security
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computing (HPC) systems, including GPUs, and programming, such as using CUDA, MPI, AI/ML/DL, and advanced debuggers and performance analyzers. Familiarity with working on open-source projects. About UF
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tensor networks and matrix product states Expertise in quantum platform software such as Qiskit or cuda-q Other Information: While principally based at Brookhaven National Laboratory, candidates should be