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FPGAs, CGRAs, and many Machine Learning accelerators, offer significant opportunities for improving performance and energy efficiency compared to traditional CPUs/GPUs. Yet, porting and optimizing code
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the development of scalable software tools and pipelines, potentially leveraging GPU/FPGA accelerators. Our aim is to build next-generation molecular atlases for chronic diseases and to improve patient
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-following inverters. Implementing and optimizing scalable algorithms for transient and stability analyses on HPC architectures (CPU, GPU, hybrid). Enhancing the numerical robustness and efficiency of existing
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(€39,005.40 gross/year, 30 hours/week) Access to a modern GPU cluster Conference travel and active support towards publications How to apply Email us with your CV, a GitHub repo or code sample, and a short
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of visualisation, machine learning / AI, and human-computer interaction Very good programming skills (web-based visualisation, Python, and/or GPU programming) First experiences in the participation in research
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(3–4 years) Salary per university collective agreement (€39,005.40 gross/year, 30 hours/week) Access to a modern GPU cluster Conference travel and active support towards publications How to apply Email
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Website: https://ilijabogunovic.com/rhine-ai/ The Rhine AI (Reasoning, Human
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using clusters like UPPMAX and GPUs for high-performance computing and parallel computing using clusters like UPPMAX and GPUs for high-performance computing are essential. While not required, experience
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FPGAs, CGRAs, and many Machine Learning accelerators, offer significant opportunities for improving performance and energy efficiency compared to traditional CPUs/GPUs. Yet, porting and optimizing code
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Computational Imaging Research Lab (CIR), Department of Biomedical Imaging and Image-guided Therapy | Austria | 2 months ago
imaging datasets across modalities (X-ray, ultrasound, MRI). Scalable ML workflows: GPU-based training, experiment tracking, reproducible pipelines, model validation and deployment. Research excellence