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Field
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3T Siemens MR scanners, OPM-MEG, EEG, eye tracking, and TMS laboratories. They will also have access to Princeton's world-class computational infrastructure, including GPU systems capable of running
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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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-dimensional biological datasets. Familiarity with GPU computing and high-performance computing (HPC) environments. Other Requirements Ability to work collaboratively with researchers across computational and
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documentation of HPC architectures, configurations, and operational procedures. Guide the architecture of the next-generation of GPUs through an intuitive and comprehensive grasp of how GPU architecture affects
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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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. This is not a remote position. Additional Information Competitive compensation package with attractive work conditions. Access to state-of-the-art research facilities and GPU cluster. Opportunities
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inference pipelines using modern ML tooling (e.g., PyTorch/TensorFlow/JAX), version control, containers, and HPC/GPU resources. Support the publication of intermediate data products, models, code, and
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-term project. We are looking for a software engineer to develop new features and extend the capabilities of a real-time neural data processing and decoding platform. This includes optimizing GPU
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platforms, GPU environments or scientific computing. Experience in EU-funded projects or international collaborations. Experience working with learning management systems or digital learning tools. Knowledge
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Selectris energy filter and Falcon 4i. The Crick Institute has excellent High Performance Computing resources, dedicated high-speed data storage and CPU and GPU clusters. In collaboration with the Cryo-EM