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, versioned RESTful API (e.g., FastAPI/Flask) for inference; and Containerise services (Docker), set up CI/CD pipelines, and deploy for inference on GPU/CPU servers. Data engineering, Governance, and
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that outperforms highly optimized code written by expert programmers and can target different hardware architectures (multicore, GPUs, FPGAs, and distributed machines). In order to have the best performance
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expertise in key machine & deep learning frameworks and toolsets. Experience in GPU computing, HPC, Containers & Image processing tools would be appreciated. A strong track record of publications in high
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Do: Contribute to one or more NESAP workflow projects (https://www.nersc.gov/what-we-do/support-for-scientists/nersc-science-acceleration-program/nesap-for-doudna) using NERSC HPC resources, edge
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limited to deep learning Experience utilising GPU enabled High-Performance Computing environments is an asset Open minded critical thinker, willing to actively contribute to the further development of multi
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managing experiments using GPUs Ability to visualize experimental results and learning curves Effective inter-personal and team-building skills Self-motivated with an ability to work independently and in a
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of this, Tiramisu can generate fast code that outperforms highly optimized code written by expert programmers and can target different hardware architectures (multicore, GPUs, FPGAs, and distributed machines). In
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. Experience with graph-based data analysis or anomaly detection methods. Exposure to high-performance or GPU-based computing environments. Demonstrated ability to contribute to publications or technical reports
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tracking), dataset curation, HPC/GPU programming, blockchain for secure data, C-family languages, and embodied AI/robotics are a plus. Experience with general network resilience, cellular automata
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resources of TU Delft, ranging from personal machines, to shared GPU servers, the Delft AI Cluster that is shared across departments, as well as DelftBlue , which is one of the top 250 supercomputers in