37 assistant-professor-and-human-computer-interaction Postdoctoral positions in Australia
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on projects in this area, with assistance and mentoring from senior academic staff familiarity with the theoretical methods of quantum error correction, and the theory of fault-tolerant quantum computing
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electronics and a strong passion for developing space payloads and collaborating on space projects with external partners, including space agencies. This position is part of a team led by Professor Paulo de
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fundamental research and the pharmaceutical and biotechnology industries. The research program is co-funded by the Australian Research Council and the Sydney- and US-based biotech company INSAMO and the
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ARC Industry Laureate Post Doctoral Research Associate in Effecting Solutions for Risk to Remote Ind
Health and well-being services including a leading Employee Assistance Program Comprehensive career development and training, plus a 25% discount on UniMelb graduate courses for your immediate family
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$101,332 to $113,918 Fixed term (2.5 years), Full-time, Joondalup Campus Open to candidates with relevant work rights POST DOCTORAL RESEARCH FELLOW (ARTIFICIAL INTELLIGENCE) COMPUTING AND SECURITY
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Employee Assistance Program For more information check out our benefits page! Your New Team – “ARC Centre of Excellence for Transformative Meta-Optical System“ https://www.tmos.org.au/work-with-tmos/ TMOS
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Employee Assistance Program For more information check out our benefits page! Your New Team – “ARC Centre of Excellence for Transformative Meta-Optical System“ https://www.tmos.org.au/work-with-tmos/ TMOS
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participation in conferences and workshops, and your active engagement with industry partners. This role reports to Associate Professor Ailar Hajimohammadi, and has no direct reports Level A, Salary - $110,059 to
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sedimentologic and structural, on the subsurface flow and interactions of hydrogen upon injection and subsequent recovery. This postdoctoral research associate will work in collaboration with colleagues
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-carbon electric power systems, taking into account wake interactions between individual wind turbines. The project focus is on how to generate and utilize reduced-complexity predictive models for windfarm