Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Monash University
- Australian National University
- The University of Queensland
- University of New South Wales
- La Trobe University
- University of Adelaide
- AUSTRALIAN NATIONAL UNIVERSITY (ANU)
- Queensland University of Technology
- The University of Western Australia
- UNIVERSITY OF MELBOURNE
- University of Melbourne
- University of Western Sydney
- RMIT University
- 3 more »
- « less
-
Field
-
(PhD entry level $105,518 p.a.) Join a collaborative and cutting-edge research environment working with world-class researchers. Apply statistics, bioinformatics, and machine learning methods to analyse
-
(PhD entry Level - $108,156 p.a.) Join a collaborative and cutting-edge research environment working with world-class researchers. Apply statistics, bioinformatics, and machine learning methods
-
software and advanced computer skills. Demonstrate the ability to learn new techniques quickly and deliver work in a timely manner. Have experience with advanced mass spectrometry platforms, particularly
-
to work collegially with other staff in the workplace. Demonstrated computer literacy and proficiency in Microsoft Office and specified university software programs, with the willingness to learn new
-
including the application of artificial intelligence and machine learning. You will engage with industry, government, and research collaborators, fostering partnerships that deliver outcomes aligned with
-
and research experience on the structural health monitoring of bridges. Applicants are expected to have experience in structural health monitoring, digital twins, AI and machine learning. A track record
-
functionalisation, fabrication and characterisation of carbon materials for application in solar cells Predication and discovery of new materials for next generation solar cells driven by machine learning
-
for research leadership. Conceptualisation and completion of data analyses with skills related to epidemiology, biostatistics and modelling including machine learning methods A sound understanding of clinical
-
one or more of the following areas: complex quantum processes, quantum error corrections, tensor networks, optimisation and machine learning, and developing software infrastructure Some experience in