Student topics

High fidelity mathematical models of biological phenomena are often complex and can require long computational runtimes which can make computational inference for parameter estimation intractable.  In this project we will overcome this challenge by working with computationally simple low fidelity models and build a simple statistical model of the discrepancy between the high and low fidelity models.  This approach provides the best of both worlds: we obtain high accuracy predictions using a computationally cheap model surrogate.

Study level

PhD, Master of Philosophy

Faculty

Faculty of Science

School

School of Mathematical Sciences

Research centre(s)

Centre for Data Science

Robotic Process Automation (RPA) is becoming a popular choice for organisations to support their digital transformation and to maintain operational resilience. Many organisations are keen to adopt Robotic Process Automation (RPA) to dramatically improve operational efficiency. Many organisations train and assign their staff as “citizen-developer” to design, test, and maintain the bots using Low-Code/No-Code platforms. However, there are number of issues surfaced when using organisational employees as citizen developer ranging from technical & process capabilities to scalability of RPA.

Study level

Master of Philosophy, Honours

Faculty

Faculty of Science

School

School of Information Systems

Research centre(s)

Centre for Data Science

This project seeks to leverage recent advances in machine vision and natural language processing algorithms to support the design and development of knowledge-driven applications that support communication and collaborations with their users.One particular area where this will be investigated is in workplaces for supported employment, that is employment opportunities for people with intellectual disability. One of the questions to address is how machines could respond to what a user shows them in order to assist with decision making in a …

Study level

PhD, Master of Philosophy, Honours

Faculty

Faculty of Science

School

School of Computer Science

This project is about using neural network models help us understand Anti-Microbial Resistance (AMR), a phenomenon in which bacteria adapt to reduce the effectiveness of antibiotics, usually through a process known as Lateral or Horizontal Gene Transfer - where genes are included in the organism from other sources.Our focus will be on learning compact vector representations of biological sequences known to be associated with AMR genes. By encoding DNA sequences in this way we can more rapidly identify AMR genes …

Study level

PhD, Master of Philosophy, Honours

Faculty

Faculty of Science

School

School of Computer Science

Research centre(s)

Centre for Data Science

Soils are now in the ‘front line’ of global environmental change. Soils are the largest global pool of actively cycling organic C and N. Maintaining and increasing soil organic matter (SOM) is a prominent strategy for mitigating atmospheric CO2 and adapting agriculture to climate change.At the same time the global biodiversity crisis has led to increased scrutiny on supply chains to scrutinise farms ecological footprint. Planting or retaining trees in the landscape has the opportunity to achieve both outcomes, however …

Study level

Master of Philosophy, Honours

Faculty

Faculty of Science

School

School of Biology and Environmental Science

Research centre(s)

Centre for Agriculture and the Bioeconomy

The CRISPR-Cas9 technology allows the modification of virtually any gene in any organism of interest. It has generated a lot of interest, both in the research community and the general population.One of the crucial components of CRISPR experiments is the design of the 'guide RNAs' that will control where modifications occur. We have developed a software pipeline, named Crackling, to identify safe and effective guide RNAs across entire genomes.We are seeking to expand and improve various aspects of our current …

Study level

Honours

Faculty

Faculty of Science

School

School of Computer Science

Research centre(s)

Centre for Data Science

Microfluidic devices (MFD) are extensively used in microbial studies. Bacterial cell attachment onto surfaces under flow conditions in laminar regime has been previously studied using a custom designed MFD.As an extension of this study, microbial attachment under turbulent flow is to be studied in a future project. The suitability of current MFD for microbial studies under turbulent flow must be evaluated to adopt / redesign the MFD.A computational fluid dynamics (CFD) analysis is proposed to examine the fluid flow inside …

Study level

Honours

Faculty

Faculty of Engineering

School

School of Mechanical, Medical and Process Engineering

Videos of sport activities are widely available at large scales. AI and its sub-fields, especially computer vision and machine learning, have a great potential to analyse, understand and extract useful information from these videos.This project aims at using AI and its subfields in computer vision and machine learning to develop techniques for analysing sport videos to extract intelligence for players and coaches.

Study level

PhD, Master of Philosophy, Honours

Faculty

Faculty of Engineering

School

School of Electrical Engineering and Robotics

The missing data problem is often unavoidable for real-world data collection systems because of a variety of factors, such as sensor malfunctioning, maintenance work, transmission errors, and so on. Filling in missing information in a dataset is an important requirement for many machine-learning algorithms that require a complete dataset as input. Data imputation algorithms aim at filling the missing information in a dataset. Many missing data imputation techniques exist in the literature, with applications demonstrated on various types of datasets. …

Study level

PhD, Master of Philosophy, Honours

Faculty

Faculty of Engineering

School

School of Civil and Environmental Engineering

Research centre(s)

Centre for Data Science

Cancer is an extremely heterogeneous disease, particularly at the cellular level. Cells within a single cancerous tumour undergo vastly different rates of proliferation based on their location and specific genetic mutations. Capturing this stochasticity in cell behaviour and its effect on tumour growth is challenging with a deterministic system, e.g. ordinary differential equations, however, is possible with an agent-based model (ABM). In an ABM, cells are modelled as individual agents that have a probability of proliferation and movement in each …

Study level

Master of Philosophy, Honours

Faculty

Faculty of Science

School

School of Mathematical Sciences

Research centre(s)

Centre for Data Science

In the age of Big Data, machine learning methods, and modern statistics the adage "all models are wrong but some are useful" has never been so true. This project will investigate data science approaches where more than one model makes sense for the data. Is it better to choose a single model or is there something to be gained from multiple models?This project will look at variable selection methods, penalised regression, Bayesian model averaging and conformal prediction. The research has …

Study level

Honours

Faculty

Faculty of Science

School

School of Mathematical Sciences

Research centre(s)

Centre for Data Science

Drought is the biggest barrier to sequestering soil organic carbon (SOC) in soils over the long-term. While options are limited during dry periods, how we manage our pastures prior to drought can influence the resilience of SOC to losses and enhance recovery.

Study level

PhD, Master of Philosophy, Honours

Faculty

Faculty of Science

School

School of Biology and Environmental Science

Research centre(s)

Centre for Agriculture and the Bioeconomy