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Found 3 matching student topics

Displaying 1–3 of 3 results

Smart triggered sampling: low-cost devices and intelligent retrofits for capturing the moments that matter

Many water quality issues are event-driven. The most informative signals often appear during short windows associated with storms, illicit discharges, first flush, or operational upsets. Capturing these windows is genuinely hard. Manual sampling is often too slow, especially overnight or during fast-changing events. Conventional autosamplers help, but they are large, power-hungry, and typically deployed only at major assets, leaving smaller drains, tributaries, pump stations, and pollution hotspots without coverage. Even when an event is captured, fixed-interval sampling fills bottles after …

Study level
PhD
Faculty
Faculty of Engineering
School
School of Civil and Environmental Engineering

Continuous time samplers (MCMC at the limit!)

The goal of this project is to develop new continuous time Monte Carlo methods for efficient sampling from high-dimensional distributions. Continuous-time Monte Carlo methods are a class of algorithms that use continuous-time dynamics to generate samples from target distributions, rather than the discrete-time dynamics used in traditional Markov chain Monte Carlo (MCMC) methods. These methods have been shown to have faster mixing and better exploration of the state space, making them particularly appealing samplers for challenging distributions.The main objectives of …

Study level
PhD, Master of Philosophy, Honours
Faculty
Faculty of Science
School
School of Mathematical Sciences
Research centre(s)
Centre for Data Science

Optimising sampling design for model discrimination of coral reef recovery

Natural disturbances including severe storms and bleaching events have devastating impacts on the Great Barrier Reef's health. Unfortunately, the increasing pressures associated with climate change are causing these disturbances to occur more frequently, for a longer duration and with more intensity.It's essential to understand the recovery dynamics between major disturbances so we can manage the health of the Great Barrier Reef under increased environmental pressures. Many studies modelling reef recovery assume a specific form for the growth dynamics. However, the …

Study level
PhD, Master of Philosophy, Honours
Faculty
Faculty of Science
School
School of Mathematical Sciences
Research centre(s)
Centre for Data Science

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