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Field
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, such as, geometric/topological/algebraic data analysis, geometric/topological deep learning, Math for AI, categorical deep learning, sheaf neural networks, PINN/KAN models, neural operators, etc, and
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depends on the background of a suitable candidate. The main topics of the group in the past few years were generative modeling, 3D reconstruction, image-editing, and deep learning using 3D data. More
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populations and biobanks for risk prediction, genetic discovery, and genomic medicine. Federated and transfer learning for distributed and privacy-preserving data integration. AI and Deep learning approaches
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deep learning. You will contribute to several high-impact projects addressing hydrological extremes and their feedbacks within climate and human systems. Your work will have real-world impact, providing
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to develop deep learning models for analyzing whole-slide histopathology images, as well as natural language processing (NLP) methods for clinical records such as pathology reports and electronic health data
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for scent signals. Prior research experience and track record in signal detection, machine learning and deep learning. Prior programming experience in state-of-the-art AI techniques. Mastering of a
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polarisation shaping, and finally using deep learning to solve the complexity challenge associated with coherent beam combination. The role Within HiPPo, your specific task will be to develop a ‘digital fibre
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.) during medical imaging and radiation therapy treatments. At least one position will be offered to a researcher that has experience in deep learning and AI development and a willingness to apply these
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radiation therapy treatments. At least one position will be offered to a researcher that has experience in deep learning and AI development and a willingness to apply these approaches in radiation therapy
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genomic, epigenomic, and fragmentomic data, from patient liquid biopsy samples Design and evaluate deep learning models for MRD detection and characterization Collaborate with multidisciplinary teams across