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Field
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: Artificial Intelligence / Machine Learning Knowledge Representation and NLP methods Clinical Informatics Bioinformatics Biomedical Ontology Public Health Informatics Nursing Informatics Imaging Informatics
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on accelerating high-impact advances in human performance by leveraging the rapidly advancing fields of bioengineering, neuroscience, artificial intelligence, genetics, molecular biology, imaging, and regenerative
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Description Are you passionate about advancing robotics and artificial intelligence? The Robot Learning & Control laboratory in the Division of Engineering at NYU Abu Dhabi is seeking a highly
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well as artificial intelligence and machine learning techniques (AI/ML) with emphasis on electronic properties (charge and spin) of a range of materials important to the DOE mission, including the materials classes
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, separation, and catalysis, with a focus on carbon capture and conversion technologies. Artificial Intelligence: Leveraging AI and machine learning to optimize material design and catalysis processes. Carbon
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theory, statistical physics, and the theory of complex systems. A particular focus is given to interdisciplinary fields such as cognitive and brain sciences, quantum computing, artificial intelligence and
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-based sensor data to enhance the prediction of peatland soil properties and functions. You will focus on leveraging machine learning/deep learning techniques along with explainable artificial intelligence
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-based sensor data to enhance the prediction of peatland soil properties and functions. You will focus on leveraging machine learning/deep learning techniques along with explainable artificial intelligence
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and networking, with a particular focus on 5G and Beyond 5G technologies and applications of artificial intelligence and machine learning to wireless systems. The candidate will oversee the design and
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and explainable hybrid Artificial Intelligence, i.e., the mix of formal knowledge representation and reasoning with sub-symbolic data-driven machine learning approaches, to work on car-driver digital