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to establish improved, AI-enhanced calibration strategies for diverse industrial and geophysical materials. Ultimately, it seeks to determine the optimal, AI-informed approach for selecting and calibrating
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awareness These funded PhD scholarships are suitable for students with a background in Computer Science, Mathematics, Engineering and Cognitive Science. Students with interests in machine learning, deep
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Duration: Doctor of Philosophy (PhD) - 3.5-year fixed-term appointment Master of Engineering Science (Research) - 2-year fixed-term appointment Remuneration: The successful applicant will receive a tax-free
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, machine learning, and photonics. Be part of a multidisciplinary research team spanning science and engineering. Access state-of-the-art laboratories and high-performance computing facilities. Gain
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University of Technology (QUT, Brisbane, Australia) related to machine learning for particle laden fluid mechanics. QUT is a major Australian university with a global outlook and a 'real world' focus. We
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systems with "self-diagnosis" and "self-healing" capabilities. By integrating federated learning, graph neural networks, and blockchain technology, we will develop a framework that moves beyond static
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Electrical Engineering, Computer Science, or a related discipline. A research-oriented attitude. Solid background in machine learning and optimization methods. Knowledge and experience in (wireless
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Engineering, Mechatronics, or Robotics, with a heavy emphasis on dynamic system theory, or a closely related discipline. Strong academic background in applied intelligent control techniques, machine learning
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on building “bicycles for the mind”, algorithms that enhance (rather than automate) human capabilities. The Machine Learning Researcher serves as a computational scientist and technical lead, supporting
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tasks to work with, enhance or replace established methods from computational engineering and computer simulation (such as the finite element method and constitutive materials models) to represent and