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Field
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develop new features and extend the capabilities of a real-time neural data processing and decoding platform. This includes optimizing GPU-accelerated signal processing pipelines, improving system
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or TensorFlow. Practical background in training and validating models on GPU-based and distributed computing environments. Working knowledge of containerization tools and orchestration platforms (e.g. Docker
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datasets and run experiments on HPC infrastructure (GPU clusters, SLURM). Strong written communication skills for technical documentation, reporting, and research outputs. Ability to work independently and
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environments. Experience with parallel computing environments, HPC in a Linux environment. Experience with surrogate modeling. Experience with data analytics techniques. Familiarity with C++ and GPU programming
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computing environment that includes GPU clusters, large-memory servers, and an NVIDIA DGX B200 system. These resources support the training of large multimodal models involving audio, video, language
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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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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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., Bayesian, hierarchical, time-series), experience with sensor-based data (such as eye tracking, EEG, and heart rate), and proficiency in computational workflows, including distributed and GPU-based systems
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them. Research Computing & AI Enablement - Work with the Architecture team to build scalable HPC and GPU-enabled environments on IaaS cloud sites along with other specialized hosted solutions. Work
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, forward-looking, and varied research fields and projects, with numerous development opportunities Modern hardware and infrastructure at the workplace, from compute and GPU servers to supercomputers