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This PhD scholarship is funded by an Australian Research Council Industry Fellowship grant. It is a 3.5-year research training program. The ARC Industry Fellowship program aims to develop a strong
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 23 days ago
SynBio Facility Director. Plant SynBio Australia is seeking to change the plant synthetic biology landscape for Australia by developing and applying new technologies to fields where they are currently not
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are desirable. To be successful in this position, you’ll have: A PhD degree in Computer Science related field is desirable Industry Experience: Demonstrated industry experience in managing, building, and testing
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development of micropropagation for vanilla, as part of a CRC-P industry linked project. They will organise and conduct experiments, manage project administration and reporting, coordinate with industry
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 28 days ago
SynBio Facility Director. Plant SynBio Australia is seeking to change the plant synthetic biology landscape for Australia by developing and applying new technologies to fields where they are currently not
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of Adelaide and La Trobe University. The ANU Plant SynBio Facility will be led by the ANU Plant SynBio Facility Director. Plant SynBio Australia is seeking to change the plant synthetic biology landscape
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 28 days ago
Industries (NSW DPI), based in Wagga, NSW. The successful candidate will work within the Research School of Biology, reporting to Professors Danielle Way and Bob Furbank, in collaboration with Dr Harsh Raman
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Fellow to join a collaborative research project on canola harvest index with the NSW Department of Primary Industries (NSW DPI), based in Wagga, NSW. The successful candidate will work within the Research
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the largest computer vision and machine learning research group in Australia with over 180 members including academics, research staff and students. The AIML works on a mixture of fundamental and
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academics, research staff and students. The AIML works on a mixture of fundamental and commercially orientated research projects in computer vision and machine learning. To be successful you will need: • A