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. Machine-learning and deep-learning techniques will be developed and compared for cancer-risk prediction, classification and prognostic modelling. Explainable and Multimodal AI Explainable AI (XAI) methods
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Machine learning is moving beyond centralised data and single models. Deep variational and decentralised learning sit at the frontier of this shift, and the theory that underpins them is still being written
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Apply Now Job Summary Developing AI-based wireless communication systems: dataset generation, deep learning model development, and optimization for intelligent precoding in multi-user visible light
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collaborative learning processes in specific knowledge domains in educational sciences and AI tools and infrastructures. Different types of AI (generative and domain specific intelligent technologies) change
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and, where appropriate, medical imaging and longitudinal data. A range of machine-learning and deep-learning techniques will be developed and evaluated for cardiovascular disease prediction and risk
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interface of machine learning, deep learning, data science and applications in forest sciences. Together with the Director, you will further develop KIForst as a faculty-wide platform for methodological
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/learning process and for individual students Applying your skills and experience to your assigned teaching schedule. Participating in and contributing to assessment activities of courses/curriculum and
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, role model and facilitator who provides guidance and academic support for students. Demonstrating enthusiasm for teaching, the teaching/learning process and for individual students. Applying your skills
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related to heart failure and cardiovascular biology. Develop and apply machine-learning and deep-learning approaches to identify disease-associated cardiomyocyte subtypes, cellular trajectories, and
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Join the forefront of groundbreaking research at the Beckman Research Institute of City of Hope, where we are changing lives and making a real difference in the fight against cancer, diabetes, and