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Computational Imaging Research Lab (CIR), Department of Biomedical Imaging and Image-guided Therapy | Austria | 3 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
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computing and the use of GPU clusters. Entry Requirements Acceptable first degree - Computer Science/Physics/Maths The standard minimum entry requirement is 2:1. First class in bachelor degree or a master
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on air-based cooling systems, they increasingly reach their thermal limits due to rapidly rising power densities in modern CPUs and GPUs. Liquid cooling technologies, such as Direct-to-Chip (D2C) can
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-tuning only a small set of low-rank matrices for each agent role, drastically reducing GPU memory and training time while preserving the model's pre-trained knowledge. The primary outcome of this research
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segmentation." CVPR. 2022. [3] van Spengler, Max, and Pascal Mettes. "Low-distortion and GPU-compatible Tree Embeddings in Hyperbolic Space." ICML. 2025. [4] Pal, Avik, Max van Spengler, Guido Maria D'Amely di
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/ computer vision and pattern recognition, including but not limited to biomedical applications Strong interest in applied machine learning, including but not limited to deep learning Experience utilising GPU
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without reliance on vision. The work will combine auditory perception models, predictive processing and flight control, validated through simulation and experimental flight tests on an embedded GPU platform
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GPU-capable, parallelized simulation frameworks. Work closely with experts in HPC and power systems to enhance scalability and computational performance. Disseminate your findings through scientific
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) Programming skills in C/C++, Rust or other relevant low-level languages is desirable. Experience programming GPUs, FPGAs, ASICs or other specialised hardware is desirable Studentship and eligibility
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simulation workload and update the solver data structures when the mesh changes. These approaches would be applied on modern large-scale heterogeneous parallel computing environments where both CPUs and GPUs