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Australian National University | Canberra, Australian Capital Territory | Australia | about 2 hours ago
available at the John Curtin School of Medical Research (JCSMR), within the ANU College of Science and Medicine. This role supports computational analysis of high-resolution spatial transcriptomics and single
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and postgraduate students. The School is also responsible for the supervision of ~120 PhD students, reflecting research strengths and expertise in the disciplines of Animal Biology, Computational
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for drug discovery. Publishing peer-reviewed research and contributing to industrial software tools. About You To be successful in this role, you will have: A PhD in machine learning, computer science
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for the design of therapeutic RNA candidates. The successful candidate will be based in the Centre for Computational Biomedical Sciences and will contribute to the delivery of the NTRP's mission to grow
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-on experiences, industry placements and research at JCU in the areas of Marine Biology & Aquaculture, Zoology and Ecology, Earth and Environmental Sciences, Information Technology, Physical Sciences, and
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Job Reference:1041374 Are you a hands-on science enthusiast with a flair for research, lab work, and data analysis? Join a world-class research team driving innovation in resource recovery and
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and postgraduate students. The School is also responsible for the supervision of ~120 PhD students, reflecting research strengths and expertise in the disciplines of Animal Biology, Computational
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Completion or near completion of a PhD in Engineering (mechanical, aerospace, civil, petroleum) or Science (applied physics, applied mathematics) with a strong background in CFD and the finite element method
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of Mechanical and Mining Engineering, where innovation meets impact. As a key member of this dynamic team, you will develop cutting-edge computational models of two-phase flow in fractured media — ranging from
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Quantitative Genetics, Statistical Genomics, Computational Biology, Plant Breeding, or a related field. Strong expertise in GWAS and post-GWAS analysis (e.g., fine mapping, gene network modelling, GWAS boosting