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languages; experience with GPU programming (e.g., CUDA) is highly desirable. Background in optimization, image-guided radiotherapy, medical imaging, or computational modeling. Experience with treatment
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collaborative, international team. We offer Cutting-Edge Resources: Access to state-of-the-art compute and GPU infrastructure, including H100 and B300 GPU clusters. Innovation: The opportunity
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in GPU programming one or more parallel computing models, including SYCL, CUDA, HIP, or OpenMP Experience with scientific computing and software development on HPC systems Ability to conduct
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). Practical experience with cloud computing platforms (e.g., AWS, GCP, Azure). Additional Qualifications Experience with multi-GPU model training and large-scale inference. Familiarity with modern AI
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: Knowledge on floating point arithmetic and mixed/reduced precision computing techniques Experience with programming GPUs and/or other accelerators Proficiency in mathematical reasoning and numerical analysis
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computing Hands-on experience with PyTorch, including GPU-accelerated model training and optimization Experience training and running models on shared HPC clusters and remote GPU servers, including working in
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disease insights. The lab has state-of-the-art computing capabilities with an in-house cluster serving 80 CPU cores and 1.5TB of RAM, as well as a newly acquired NVIDIA DGX box with eight H100 GPUs and 224
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learning, medical image computing, biomedical engineering, medical physics, or related field Strong Python and PyTorch experience Solid publication record and ability to communicate research results
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variety of computational devices (e.g. CPUs and GPUs) while ensuring overall consistency and performance. - contribute to identify new CSE applications domains, such as condensed matter systems, quantum
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geophysical sciences, computer science, or machine learning with 0 to 2 years of experience Knowledge of deep learning, PyTorch/JAX, and scaling deep learning models to large GPU-based machines Technical