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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 4 hours ago
& PEWER Academic Level B Research Fellow (Computer Science) Engage with world-class computing infrastructure and work in partnership with the Australian government Develop sophisticated AI approaches
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Research Fellow - Environmental Informatics Hub Job No.: 680160 Location: Clayton campus Employment Type: Full-time Duration: 2 year fixed-term appointment (with the possibility of an additional 2
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or chemistry, or applied mathematics. Demonstrated evidence of computational modelling experience, including code development and data analysis. Ideally experience using one or more of the following: Molecular
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in the relevant discipline area, such as computational/theoretical physics or chemistry, or applied mathematics. Demonstrated evidence of computational modelling experience, including code development
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the other members of the wider project team. To be successful in this position, you will have: PhD qualification in the relevant discipline area, such as computational/theoretical physics or chemistry, or
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outputs of a team dedicated to translating discovery into meaningful impact for people living with Friedreich Ataxia. We are seeking someone with a PhD in computer engineering, biomedical engineering
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their best work. The School of Biological, Earth and Environmental Sciences (BEES) examine the natural environment, including life, from the core of the Earth to its upper atmosphere, deep in time through
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a Postdoctoral Research Fellow in Soil Science, you’ll play a role in research into the behaviour of recycled phosphorus fertilisers in soil. You’ll work within an innovative research program
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 7 days ago
Australia Application Deadline 6 Dec 2025 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job
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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