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Field
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Computing (HPC) and the Lattice-Boltzmann Method (LBM), a mesoscopic computational method, to model flows over a variety of engineered surfaces. You will investigate how engineered surfaces can delay flow
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across multiple data modalities Manage HPC resources and job scheduling on NAISS Arrhenius CPU and GPU partitions Requirements To meet the entry requirements for doctoral studies, you must hold a Master’s
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reconstruction to overcome these challenges. Your tasks - develop physics-informed, self-supervised learning approaches for phase retrieval - implement reconstruction algorithms on HPC clusters for large-scale
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on HPC clusters for large-scale hierarchical tomography apply algorithms to experimental tomographic data collaborate closely with beamline scientists and project partners publish and present results in
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analysing multi-modal data. Use Anthropic and/or OpenAI APIs. Perform large-scale training and testing on an HPC server. Disseminate the research results by writing papers and presenting your work
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, utilising high-performance computing (HPC) servers and data from large-scale facilities such as Diamond Light Source and ISIS Neutron and Muon Source. For informal enquiries please contact Dr Jay Warnett
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Computation (HPC) infrastructure • Excellent scientific infrastructure • Participation in project meetings and international conferences • Research stay abroad • Flexible working hours and various
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to support model development. Accordingly, you will gain access to the high-performance computing (HPC) facilities at QUT.
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or population genetics theory Evidence of technical skills and interest (e.g., writing code, using git, HPC experience) Understanding of basic statistical methods Demonstrated ability to review and synthesize
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interest (e.g., writing code, using git, HPC experience) Understanding of basic statistical methods Demonstrated ability to review and synthesize literature into scientific concepts About us The Department