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proteostasis, cytoskeletal dynamics, and ferroptotic cell death. This collaborative work combines biochemistry, chemical biology, cell signaling, live-cell imaging, and organotypic brain slice models to dissect
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Inria, the French national research institute for the digital sciences | Rennes, Bretagne | France | 2 months ago
machine learning. Gathering this large panel of skills, the team aims at improving our understanding, reconstruction and forecasting of ocean dynamics, and more specifically to bridge model-driven and
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analysis, computer vision, or machine learning, with a clear interest in developing image analysis algorithms and an affinity with medical topics. Good communication and organizational skills are essential
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baths, monitoring dye uptake, and evaluating color responses. Beginning or intermediate experience with statistical modeling or machine-learning methods, especially for exploring multivariable
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energy systems and demand response; process and energy system modeling; optimization and model predictive control; and applications of artificial intelligence and machine learning to energy systems
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License/Registration/Certification n/a Physical Requirements Some standing or walking. Sitting at computer workstation for extended periods. Repetitive motion. Lifting, pushing, or pulling of objects up
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research career. Research and technical training Training will include hands-on work in the following areas: Developing and validating deep-learning and machine-learning models using echocardiography
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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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machine learning and Bayesian calibration methods to enable multi-scale, multi-physics model development. Complete simulation verification, model validation, uncertainty quantification, and documentation
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well as exploring the application of research findings to advanced 3D models such as organoids and 3D bioprinted tissues Learning about high-content, automated phenotypic drug screening pipelines against high