Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
-
Field
-
contribution of 17% superannuation applies. Fixed term, full-time position for 3 years. To unlock the great potential of hydrogen as a future fuel, a critical challenge is the cost of storage at scale, without
-
the cost of storage at scale, without significant loss. The most economically viable options for large-scale hydrogen storage are underground, within solution-mined or engineered caverns and depleted
-
successful you will need: A PhD in Petroleum Engineering or related fields, with an established record of publications in international peer-reviewed journals. Demonstrated skills and experience in core
-
multilayer PCB, FPGA programming, embedded systems, and preferably ASIC-design. Knowledge in Systems Engineering, particularly in Space and Defence is highly regarded. You will also demonstrate personal
-
degradation system as new drugs to combat tuberculosis Base Salary Level A, $109,301 - $116,679 p.a. + 17% superannuation About the opportunity The School of Chemistry welcomes applications for a Postdoctoral
-
B position $123K - $145K base (+ 17% Super, leave loading) Location: Kensington campus, Sydney About the role: UNSW Sydney is a world-leading teaching and research university, recognized globally
-
be responsible for executing and coordinating research for the Professor of Clinician Psychology. Some key skills required: A PhD in a psychology or related discipline. Demonstrated experience working
-
combatting wildlife trafficking and environmental harm. To be successful you will need: PhD in a relevant discipline such as computer science, data science, digital forensics, cyber security or a related field
-
applications for our Postdoctoral Fellowship positions across a wide range of diverse and cutting-edge fields. Opportunities exist within our well-funded research areas such as AI, Intelligent Secure Systems
-
-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