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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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, proteins, chemical structures, geospatial, oceanographic, or heath record data. Experience in CUDA GPU programming. Experience authoring open-source Python packages in PyPI. Familiarity with RESTful web
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approaches, the application of meta learning, and the integration of convex optimization layers Increase inference efficiency (e.g., GPU acceleration) and assess the applicability domain of learned algorithms
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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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About the role: The Costa Laboratory (https://profiles.imperial.ac.uk/t.costa ), in the Department of Life Sciences (https://www.imperial.ac.uk/life-sciences ), Imperial College London, invites
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commonly used on Unix systems. Additional languages or experience with libraries for utilizing GPU hardware efficiently, e.g., CUDA, are a plus. Experience in AI programming with, e.g., PyTorch(-DDP
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data from the European XFEL facility at DESY. Project website: https://www.mpinat.mpg.de/628848/SM-Ultrafast-XRay-Diffraction Your profile Eligible candidates have strong skills in computational
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workflows, FAISS/embedding retrieval, LLM-based parsing, RAG-style pipeline, and GPU/HPC training. Familiarity with 3D data processing or willingness to learn quickly. Publications, thesis work
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NVIDIA GPU nodes. Additionally, The CSM maintains enterprise computing infrastructure consisting of 20+ servers/devices. This includes various storage devices (TrueNAS, Synology), web servers and Windows
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fields, applying advanced techniques such as large-scale data processing and GPU-accelerated computing. Access to state-of-the-art research facilities and a new GPU cluster. Collaborative and inclusive