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responsible machine learning and/or human-AI interaction can be integrated with music-based forms of human creativity to contribute to health and well-being. The doctoral research should address one or several
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Digital Twin Framework for Smart and Sustainable Advanced Manufacturing Research area 3: Advanced Multifunctional Materials The ideal candidates would have a background in machine learning, manufacturing
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to investigate how responsible machine learning and/or human-AI interaction can be integrated with music-based forms of human creativity to contribute to health and well-being. The doctoral research should address
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at the University of Adelaide, contributing to cutting-edge research in computer vision and machine learning for space applications. This role focuses on advancing machine learning and computer vision research, with
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in each crop area and learn basic agronomic, data collection, and plant breeding methodologies in trials and nurseries planted at the USDA-ARS. Learning Objectives: The project assignments will provide
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leader in the research project - “Control of permanent magnet machines for improving system efficiency of domestic air-conditioners”. Qualifications Applicants should have a doctoral degree or an
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of genes and proteins as regulators of physiological or immunological traits. Learning Objectives: Under the guidance of the mentor, the candidate will gain experience in and learn to utilize a functional
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: Masters/Ph.D. degree in Electrical/Computer Engineering, specialized in Power Systems/Renewable Energy Planning/Optimization Expertise in medium and low voltage network modelling, power-flow, and large
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. In addition, you must have: a solid foundation in energy technology and a strong understanding of artificial intelligence (AI), machine learning (ML), and data-driven modeling documented experience
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maritime transport, marine technology, computer science, or a related field; Excellent programming skills, such as Python, Matlab, C++, or other computer languages; A record of publications in reputable peer