54 phd-studenship-in-computer-vision-and-machine-learning Fellowship positions at The University of Queensland
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simulations using DFT (particularly of surface processes); kinetic Monte Carlo simulations; molecular dynamics simulations; classical and machine-learned force fields. Highly developed skills in scientific
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to establish The National Quantum Computing Testbed Facility (NQCT). The goal of NQCT is to develop the first Australian open-access quantum computer with direct access to quantum and control hardware. NQCT
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quantum computer in Australia and aiding the Director in delivering the strategic goals for the Facility. This includes responsibilities for long-term operational planning, strategic budget management
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, cluster randomised controlled trials implementation science, data linkage, data science, machine learning and artificial intelligence. In this role, you will have the opportunity to engage in a series of
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discipline, including publications in high-quality peer-reviewed journals and presentations at major conferences. Demonstrated high-level mathematical and computer programming skills. Where PhD has been
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), clinical trials, disease surveillance, and the use of novel methods including Bayesian network, machine learning, social network analysis and dynamic data visualisation tools. Further information is
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the academic promotions process. About You Qualifications and Field of Expertise: Completion of a PhD in Systems and Synthetic Biology, Molecular Engineering, Bioengineering, Chemical Engineering, or a closely
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dynamic research environment. Key responsibilities will include: Research: Establish a research program, collaborate on research projects, seek and manage research funding, publish in reputable journals
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opportunity to make a significant impact on both fundamental science and emerging applications Key responsibilities will include: Research: Establish a research program, collaborate on research projects, seek
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technologies that support decarbonisation and clean energy goals. The successful applicant will contribute to the computational modelling and design of Prussian blue analogues, spin-crossover metal-organic