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. Expertise and knowledge in AI deep learning model development on histology whole slide imaging analysis in computational pathology is essential. Applicants should have a solid publication record and
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In this role, you will be responsible of developing cutting-edge deep learning models for real-time image and video analysis (e.g., segmentation, object tracking, reinforcement learning), with
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mechanisms available in multi-parametric MRI, we aim to establish deep learning models that predict biomarkers of diseases progression and response to therapies, with applications in brain tumours and neuro
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deep neural activity, using methods that combine physics (waves in complex media) and computational approaches. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR8552-SYLGIG-011
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models when faced with data drift, bias, and fairness challenges. The research will involve developing deep learning and synthetic data generation approaches and applying them to exemplar studies in
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for AI and deep learning (details: NVIDIA DGX-2) Intel-based Aurora Supercomputer: A next-generation supercomputing system (details: Aurora Supercomputer) Additional advanced compute architectures designed
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neuromorphic architectures; deep knowledge about our brain and how it makes decisions; coding reinforcement learning algorithms/feedback systems and simulators; electrical/electronics background with
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approaches. What we ask of you Required PhD in machine learning, physics, or a related field. Established expertise in deep learning (familiarity with graph neural networks, transformers, diffusion and flow
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approaches to remove atmospheric particulate (e.g., PM2.5) pollution. The math-based subgroup focuses on the use of deep learning and generative AI to address critical problems for the electric grid and broad
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 23 hours ago
publications or manuscripts in preparation. · Demonstrated experience applying deep learning techniques in geospatial or spatial data analysis (required). · Experience with generative AI models (e.g., diffusion