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competency models in collaboration with content experts. Develops and analyzes the Learning Needs Assessment for the department and clinical areas. Monitors compliance related to mandatory staff education
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competency models in collaboration with content experts. Develops and analyzes the Learning Needs Assessment for the department and clinical areas. Monitors compliance related to mandatory staff education
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decision-making across diverse applications in computer vision and data analysis. Where to apply Website https://aunicalogin.polimi.it/aunicalogin/getservizio.xml?id_servizio=1079 Requirements Additional
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competitive ERC. The project focuses on the development of a first-principles, machine-learning-accelerated computational framework for modelling polymorphism, anharmonicity, and electron–phonon interactions in
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image analysis packages such as Freesurfer, FSL, SPM, or 3DSlicer, or using machine learning or artificial intelligence models would be advantageous What We Offer The appointee would be exposed to ample
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School graduates over a thousand students who are ready to take on great ambitions and challenges. For more details, please view: https://www.ntu.edu.sg/eee We are looking for a Research Assistant to carry
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simulations with machine learning models derived from experimental data. 1) Functional Knowledge and Technical Expertise (50%) a. COMSOL Modeling: Design, build, and run COMSOL Multiphysics simulations to model
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limited. To learn from past warmer climates and better understand the link between climate and extremes, we can use proxy-based climate reconstructions and climate models for past warmer climates. However
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between Numerical Analysis and Machine Learning, with a focus on physics-informed machine learning. The goal is to design learning strategies that embed the structure of governing physical laws, enabling
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 7 days ago
establish reference performance and guide the design of more advanced models. The core of the work will then focus on developing and evaluating machine-learning-based NILM methods tailored