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Field
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and optimize large-scale training and inference runs for foundation models on JUPITER (multi-GPU/node, mixed precision, parallelization, I/O optimization) Integrate multimodal data sources (e.g., scRNA
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services, which provide expert support in data management and high-performance computing, including optimized pipelines and large-scale GPU resources. A competitive salary and benefits package, with
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Abilities: Experienced in heterogenous computing with GPU accelerators using one of the programming models: CUDA, HIP, SYCL, Kokkos, OpenMP, OpenACC and similar. Familiar with distributed parallel computing
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& Development Labs are the backbone of the Institute, combining scientific excellence with real-world impact. They operate within a unique ecosystem that includes the AI Foundry (state-of-the-art GPUs and
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and scripting languages. Extensive knowledge of parallel programming techniques, including shared memory and message passing parallel programming, and knowledge of GPU programming. Experience with
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and scripting languages. Extensive knowledge of parallel programming techniques, including shared memory and message passing parallel programming, and knowledge of GPU programming. Experience with
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datasets. The Research Associate will: Design and implement high-performance workflows integrating GPU programming, deep learning, and large-scale data integration. Apply advanced methods such as ColabFold
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including knowledge of PyTorch, Tensorflow, Pandas, Scikit-learn and/or Numpy. Knowledge of GPU-based computing, including multi-gpu/multi-node parallelization techniques. Fluency in spoken and written
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, OpenFOAM), and plasma physics (XGC, IPPL). Expected qualifications: A Master's degree in Computer Science or Applied Mathematics. Necessary knowledge: Modern C++, GPU computing with CUDA/SYCL, MPI, Krylov
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optimisation or machine learning (e.g., Python/Matlab/C++; PyTorch/TensorFlow). Experience in signal processing/wireless or SDR/GPU prototyping is a plus. Demonstrated research potential is highly desirable