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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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Workshops (INFOCOM WKSHPS), 2021, pp. 1–6. [4] W. Gao, Q. Hu, Z. Ye, P. Sun, X. Wang, Y. Luo, T. Zhang, and Y. Wen, “Deep learning workload scheduling in gpu datacenters: Taxonomy, challenges and vision
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100% funding per SNSF guidelines (~CHF 90'000/year) Access to modern GPU clusters and confidential-computing infrastructure Collaboration with leading researchers in AI & HPC systems and digital health
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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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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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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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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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frameworks (preferably Pytorch) Use of Linux GPU servers via command line Written and spoken scientific English It would be a plus to have familiarity with: GIS and remote sensing Internal Application form(s
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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