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Field
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-augmented generation (RAG) approaches Systems and mathematical modeling of biological or complex systems Natural language processing and machine learning Data harmonization and integration Record of research
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of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI implementation. Ability to apply AI to interdisciplinary research or developing AI models
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vision, yet their ability to acquire new expertise remains limited when training data is scarce or specialised. This project aims to develop the next generation of adaptive visual learning systems by
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. Education and scholarly development The postdoctoral associate will receive structured education in computer vision applications in medical imaging, machine learning, research methodology, responsible conduct
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, and remote-sensing data) to support model benchmarking, parameterization, and uncertainty quantification. Explore and apply AI/ML approaches (e.g., machine-learning emulators, surrogate modeling, AI
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of sensorimotor processing. We are recruiting two postdocs: Postdoc in Computer Vision & AI for Behavior Analysis Postdoc in Embodied AI (Reinforcement Learning for Motor Control) Main duties and responsibilities
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the opportunity to contribute to various cutting-edge research areas. Several exciting research topics are proposed (not limited): Computer Vision / Remote Sensing for Agriculture: Explore
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, and remote-sensing data) to support model benchmarking, parameterization, and uncertainty quantification. Explore and apply AI/ML approaches (e.g., machine-learning emulators, surrogate modeling, AI
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statistical software & machine learning (e.g., R, Python, SAS, or STATA). Experience working with large population dataset (e.g. EHR, claims data). Background in health informatics, population health research
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boson decays, searches for supersymmetry and other new phenomena, and measurements of rare standard model processes. We vigorously pursue the use of machine learning techniques for data analysis