The overarching goal of this set of projects is to understand the brain mechanisms of inter-individual variability underpinning our differences in brain rhythms and response to disturbances like sleep deprivation.
Insufficient and disturbed sleep are widespread phenomena in modern society with nearly 7.4 million Australians affected daily. Sleep loss induces adverse changes in alertness resulting in accidents and loss of life, e.g., contributing to 20-30% of fatal car crashes each year. Impaired alertness is unavoidable in occupations with shiftwork like healthcare, police, and fire and rescue service. The degree of impairment, however, is highly variable across individuals with the most vulnerable being at highest risk of accidents. Identifying vulnerable vs. resilient individuals and predicting an individual’s alertness is critical to minimising accident risks and improving safety. To address this major problem, these projects will combine biophysical modelling and big data to uncover the key brain mechanisms of individual variability in sleep, circadian rhythms and alertness in young and old individuals undergoing sleep deprivation. It is expected that models with individualised predictions will be applied in real-world to track and optimise sleep and alertness in population.
Domestic PhD stipend funded by NHMRC Ideas grant. Candidates will require quantitative background, e.g., physics, mathematics, engineering, computer science and interest in biological systems.
Most projects involve collaboration with experimentalists and/or clinicians in Australia and overseas. Some of the projects involve collaboration with industry.
HDR Inherent Requirements
In addition to the academic requirements set out in the Science Postgraduate Handbook, you may be required to satisfy a number of inherent requirements to complete this degree. Example of inherent requirement may include:
You must consult with your nominated supervisor regarding any identified inherent requirements before completing your application.
The opportunity ID for this research opportunity is 2987