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- l'institut du thorax, INSERM, CNRS, Nantes Université
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algorithms. The post-doc will carry out theoretical work on causal abstraction and causal alignment, implement algorithms and experimental pipelines in Python/PyTorch, and run experiments on GPU clusters
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 5 hours ago
. The researcher(s) will be provided access to state-of-the-art supercomputing facilities with advanced GPU and data storage capabilities. Additionally, opportunities will be available for collaborations. Duties
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will benefit from the Jean Zay supercomputer to perform intensive GPU calculations but also MARBEC/LIRMM GPU shared resources (https://umr-marbec.fr/recherche/dispositifs-de-recherche/den/ ). His/her
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Post-doctoral Researcher in Multimodal Foundation Models for Brain Cancer & Neuro-degenerative Disea
translation models and medical images analysis are considered important assets. Established expertise in key machine & deep learning frameworks and toolsets. Experience in GPU computing, HPC, Containers & Image
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simulation frameworks or HPC/GPU-accelerated ML. Proficiency in scientific software development (Python/ML stack, MATLAB for wireless simulation, reproducible workflows, version control). Strong publication
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l'institut du thorax, INSERM, CNRS, Nantes Université | Nantes, Pays de la Loire | France | 4 days ago
Devices" ). • Bring various improvements on the synthetic model (vasculature shape / aneurysm / background noise modelling) • Numerical simulations will be performed on a GPU HPC cluster. • Programming in
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typically work with datasets of up to several TBs depending on the case study); - Autonomy to conduct independent analysis and research on our (GPU/CPU) servers, familiarity with coding frames in machine
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conducted in collaboration with NVIDIA, leveraging state-of-the-art GPU-based simulation environments and AI platforms. The position will be hosted within the Medical Imaging and Robotics group led by Dr
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models. Experience in large-scale deep learning systems and/or large foundation model, and the ability to train models using GPU/TPU parallelization. Experience in multi-modality data analysis (e.g., image
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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