28 high-performance-computing Fellowship positions at The University of Queensland in Australia
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School of Electrical Engineering and Computer Science – Faculty of Engineering Part-time (40%), fixed-term position for up to 12 months Base salary will be in the range $114,824.05 - $136,048.72
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-centered AI. Publish outputs in high quality outlets, such as AAAI, IJCAI, NeurIPs, CVPR, EMNLP, CHI, CSCW, FAccT, or similar. Establish a research program in the area of human-centered decision support
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modeller to help lead a collaborative research programme between the University of Queensland and King Abdulla University of Science and Technology (KAUST) with Prof David Suggett. Our shared goal is to
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of Carbon Dioxide (GETCO₂). At our Computational Multiphysics Laboratory (CML), we value innovation, collaboration, and technical excellence. You will benefit from access to large-scale high performance
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research outputs in high quality outlets, participate in research funding applications, collaborate on joint research projects, contribute to transfer of knowledge, develop a research program, and utilise
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internationally recognised for its expertise in computational fluid dynamics (CFD), multiphase and multiphysics flow, and nonlinear solid mechanics, supported by access to world-class high-performance computing
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vital role in ensuring research excellence by supporting study directors, maintaining high-quality standards, and driving projects to meet critical milestones. This position also offers the opportunity
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-preservation, and taxonomic analysis. Proficient in purification of natural products using semi-preparative and preparative High Performance Liquid Chromatography (i.e. HPLC). Experienced in operating and
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, electrochemical, digital) into micro/nanofluidic platforms. Perform surface modification, functionalization, and patterning of materials for selective bio-interfacing. Collaborate with interdisciplinary teams
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, connectivity within and among tropical ecosystems, and the design and functioning of marine reserves. Our research integrates field studies, large-scale observations, lab-based approaches, and ecological