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to: - Developing underwater communication systems using deep learning which are well-performing to nonlinear channels. - Establishing a deep learning architecture which is optimal for underwater acoustic
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compartmental models for RSV developed within the STAMP-RSV program by tailoring an established software library for individual simulation to the Australian RSV transmission context. Information to parameterise
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qualitative research methodologies including research skills and some experience with the use of qualitative data software such as NVivo. The candidate: Must meet all Curtin University HDR enrolment
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understanding of deep neural networks by exploring the human-understandable meanings of learnt features, the evolutionary dynamics of these features across network layers, and the architectural designs
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results accurately and draw meaningful conclusions to inform further research and process improvements. Background in modelling and simulation using simulation software. Background in Techno-economic
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, conducting interviews and focus groups, developing and managing surveys, and analysing qualitative and quantitative data including skills with appropriate software would be valuable. An interest in mental
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record in medical research and innovation. The project will benefit from access to advanced computing facilities and specialized software for deep learning model development and CT scan analysis
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also need to be comfortable with online video software, and to learn and deliver the 4Ds with close supervision, review its empirical foundations, and understand and apply its theoretical principles
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developing scientific software (using any of these languages/libraries: Python, Julia, C++, C, Fortran, Matlab, Fenics/FeniX, MFem, deal.II, libMesh, PETSc, Trilinos, Pytorch, TensorFlow, Jax, Keras, Pandas