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Found 6 matching student topics

Displaying 1–6 of 6 results

Mathematical and statistical methods for change point detection in precision fermentation

Precision fermentation uses microorganisms such as yeast to produce valuable biological products for food, biotechnology and synthetic biology. A major challenge is that microbial growth and production can change when cells switch between different metabolic regimes. These changes may occur because of nutrient depletion, stress responses, dilution conditions, or shifts in how cells allocate resources between growth and product formation.This PhD project will develop new mathematical and statistical methods for detecting these metabolic change points from experimental data. The project …

Study level
PhD, Master of Philosophy, Honours
Faculty
Faculty of Science
School
School of Mathematical Sciences

Detecting metabolic regime switching using dilution-resolved growth data: mathematical modelling, statistical inference and uncertainty quantification

Microbial populations rarely grow according to a single fixed physiological program. As nutrients are consumed, waste products accumulate and environmental stress changes, cells can transition between distinct metabolic regimes associated with growth, maintenance, fermentation, respiration and survival. These transitions are biologically important and industrially relevant, but they are often difficult to detect directly from standard growth curve summaries such as maximum growth rate, lag time, carrying capacity or area under the curve.This project will develop new mathematical and statistical methods …

Study level
PhD, Master of Philosophy
Faculty
Faculty of Science
School
School of Mathematical Sciences

Optimisation of piezoelectric materials for robotics applications

Piezoelectricity, which translates to “pressure electricity”, is the phenomenon in which certain materials convert mechanical energy to electrical energy, and vice versa. Such materials are common-place and are used in a variety of applications including sensor, actuator, and energy harvesting technologies. The capabilities of such piezoelectric materials have not yet been fully realised. We plan to use computational structural optimisation to design new piezoelectric materials and components that may contribute to novel sensing technologies for robotics applications. Essentially, robots need …

Study level
PhD, Master of Philosophy, Honours
Faculty
Faculty of Science
School
School of Mathematical Sciences

Computational methods for multi-scale structural optimisation

Structural optimisation is a powerful computational methodology for finding high-performing designs for structural components or material architectures. For example, what periodic scaffold would provide the highest possible stiffness for its weight?Solving such a problem computationally requires an understanding of the relevant equations required to model the physical properties of interest, as well as efficient implementation of a range of numerical methods including finite elements, finite differences and optimisation.With recent developments in 3D printing technologies it is now becoming possible to …

Study level
PhD, Master of Philosophy, Honours
Faculty
Faculty of Science
School
School of Mathematical Sciences

Advanced numerical modelling to study fluid flow and heat transfer of ground-mounted photovoltaic panels for the power generation industry

The increase in global energy demand necessitates further advancement in photovoltaic (PV) systems. Advancements in PVs could potentially play a role to help meet the Paris Agreement of limiting global temperature increase to below 2°C.The performance of ground-mounted PV panels commonly found in solar farms depends on a myriad of factors such as tilt angle, microclimate i.e. wind loads, shading, solar irradiance, and dust deposition. This project aims to develop an advanced numerical model, namely computational fluid dynamics (CFD), backed …

Study level
PhD, Honours
Faculty
Faculty of Engineering
School
School of Mechanical, Medical and Process Engineering

Mathematical and computational models for diffusion magnetic resonance imaging (dMRI)

In 1985, the first image of water diffusion in the living human brain came to life. Since then significant developments have been made and diffusion magnetic resonance imaging (dMRI) has become a pillar of modern neuroimaging.Over the last decade, combining computational modelling and diffusion MRI has enabled researchers to link millimetre scale diffusion MRI measures with microscale tissue properties, to infer microstructure information, such as diffusion anisotropy in white matter, axon diameters, axon density, intra/extra-cellular volume fractions, and fibre orientation …

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

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