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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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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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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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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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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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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
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an emerging alternative: enhancing hard coastal infrastructure with vegetation to deliver robust protection and measurable carbon benefits. With 17% of the global coastline already hardened, understanding
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this industrial PhD studentship in Physics – fully funded by the University of Exeter and Leonardo UK. We’re looking for a student who has a passion for science, with ambition to learn and apply their own ideas
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harness advanced techniques such as machine learning, optimization algorithms, and sensitivity analysis to automate and enhance the mode selection process. The result will be a scalable methodology that
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includes an extension of up to six months if approved for your candidature. This is the full-time, tax-exempt rate which will index annually. You will receive a tuition fee offset/sponsorship, covering