Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
cancer therapies, including gene- and cell-based immunotherapies. You will work with state-of-the-art technologies such as single-cell multiomics, stem cell models, and nanotechnology, within a
-
-based algorithms (e.g., GNNs, deep reinforcement learning) design and simulate dynamic models of megaproject systems prepare and submit journal articles to high-impact publications contribute
-
ability to utilise data analytics and modelling software accordingly. Sponsorship / work rights for Australia You must have unrestricted work rights in Australia for the duration of this employment to apply
-
) in FPGA design, machine learning or a related field experience in the development of machine learning models using Python and pytorch expertise in two or more of the following technical areas: design
-
related field experience in the development of machine learning models using Python and pytorch expertise in two or more of the following technical areas: design of FPGA-based accelerators, high-level
-
) design and simulate dynamic models of megaproject systems prepare and submit journal articles to high-impact publications contribute to competitive grant proposals and research impact activities
-
applications of data science and modelling. The successful candidate will support the research of Professor Lucy Marshall, Faculty of Engineering and will collaborate with members of her cross-institutional
-
, stem cell models, and nanotechnology, within a collaborative and innovative research environment. It is an exciting opportunity to be at the forefront of translational cancer research. Key
-
the intersection of plate tectonics, critical metals, and Earth's habitability by trying to better model the Earth’s surface evolution from 1800–500 million years ago. The successful candidate will be a sedimentary
-
Diseases, School of Medical Sciences, Faculty of Medicine and Health. You will design in vitro and microfluidic-based model systems to evaluate vascular interactions related to medical device complications