QUT offers a diverse range of student topics for Honours, Masters and PhD study. Search to find a topic that interests you or propose your own research topic to a prospective QUT supervisor. You may also ask a prospective supervisor to help you identify or refine a research topic.
Found 240 matching student topics
Displaying 49–60 of 240 results
Intergenerational responsibility of the sustainable energy transition: rethinking socio-technical systems through Indigenous knowledge
As the global community works to limit global warming to 1.5°C, accelerating the transition to sustainable energy systems remains a pressing challenge (IPCC, 2023). This transition is not only technical but deeply social, with intergenerational equity emerging as a critical concern. Decisions made today regarding infrastructure, resource use, and system design will have lasting consequences for future generations (UN, 2021). Indigenous knowledge systems, which emphasise long-term stewardship and relational accountability, offer valuable insights into rethinking socio-technical systems for sustainable futures …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Information Systems
- Research centre(s)
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Energy Transition Centre
Understanding trust in renewable energy in online communities
The challenge to keep global warming to 1.5°C above pre-industrial levels has become even greater due to a continued increase in greenhouse gas emissions (IPCC, 2023). One major challenge is the shift from fossil fuels to renewable energy to reduce emissions (Gholami et al., 2016). The share of renewable energy in electricity generation has increased to 28.3%, however, an acceleration of the pace of the transition is required to limit global temperature rise (REN21, 2022).Due to the contingencies and dynamics …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Information Systems
- Research centre(s)
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Energy Transition Centre
Joint PhD QUT/Münster: Digital innovation in battery energy storage systems
Queensland is a global leader in residential solar photovoltaic adoption, yet battery energy storage uptake is comparatively low, constraining the full potential of decentralised battery energy storage systems (BESS). Similarly, in Germany, battery storage adoption remains limited and regionally concentrated, despite strong national policy support and technological advances in battery manufacturing.This project investigates the behavioural and systemic barriers to BESS adoption and explores how digital solutions can influence energy decisions. It forms part of a broader international collaboration between QUT, …
- Study level
- PhD
- Faculty
- Faculty of Science
- School
- School of Information Systems
- Research centre(s)
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Energy Transition Centre
Advanced materials for sodium storage batteries
Developing sustainable and clean energy sources (e.g. solar, wind, and tide energy) is essential to achieve the goal of carbon neutrality. Due to the discontinuous and inconsistent nature of common clean energy sources, high-performance energy storage technologies are a critical part of achieving this target.
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Chemistry and Physics
- Research centre(s)
- Centre for Materials Science
Critical evaluation of Star Formation Rate estimators in galaxies
This project aims to assess the reliability, accuracy, and limitations of various Star Formation Rate (SFR) estimators used in extragalactic astronomy. By leveraging multi-wavelength data from the ZFOURGE survey, the project will explore how well SFR indicators derived from UV, optical (Hα), infrared (IR), and radio observations compare to SED-fitted SFRs from CIGALE. The project will focus on understanding discrepancies, particularly in dusty star-forming galaxies and AGN hosts, and provide recommendations for improving SFR estimates in future research.
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Science
- School
- School of Chemistry and Physics
Power efficient computing for statistical machine learning
The carbon footprint of computing globally is estimated to be comparable with that of the aviation industry. With the advent of generative artificial intelligence, there is a growing awareness of this environmental impact both in terms of the carbon footprint and other environmental impacts including e-waste and water consumption, predominantly through the use of power-hungry graphics processing units (GPUs).These are particularly relevant issues to many fields that rely on computationally intensive simulations for data analysis or calibration of statistical machine …
- Study level
- PhD
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Hierarchical forecasting: forecasting a collection of time series
Hierarchical forecasting is a method used to generate forecasts at multiple levels of aggregation within a structured hierarchy. This technique is particularly valuable in situations where data can be organised into a hierarchy based on different dimensions, such as geography, product categories, or time. The approach ensures that forecasts at the top levels (e.g. total sales) align with forecasts at the lower levels (e.g. regional or product-level sales), creating a coherent and consistent forecasting process across the entire hierarchy.In many …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Bayesian focused learning
Forecasting renewable energy production is crucial for ensuring stable and sustainable energy grids. Traditional approaches often involve a two-stage process: first, energy production forecasts are generated, then decisions, such as how much energy to produce from various sources (wind, solar, fossil fuels), are made based on those forecasts. This disjointed process, where forecast accuracy and decision-making optimization are treated separately, can lead to sub-optimal outcomes due to conflicting objective functions.The goal of this project is to bridge these stages by …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Optimising inventory control and demand forecast accuracy though multi-objective optimisation
In today’s competitive business environment, effective inventory management and accurate demand forecasting are critical for minimising costs and maximising profitability. This project aims to address these two challenges simultaneously by applying a multi-objective optimisation approach. The primary objectives are to improve demand forecast accuracy while optimising inventory control decisions, balancing trade-offs between conflicting business goals such as minimising stockouts, reducing excess inventory, and maintaining customer service levels.Traditional approaches to inventory management and demand forecasting often treat these processes separately, which …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Decision optimisation in energy supply chain
this project aims to develop integrated forecasting and decision optimisation models for renewable energies.
- Study level
- PhD
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Probabilistic forecasting of energy
This project aims to develop probabilistic forecasting models for renewable energies vi a Bayesian approach. The models will be developed for very short term and short-term (10 minutes to 24 hours ahead).
- Study level
- PhD
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Spatio-Temporal Forecasting of renewable energies
This project aims to develop short-term (up to 24 hours ahead) forecasting models that take into account the spatial as well as temporal information in wind farms and solar farms. Such models are useful for operational planning in farms and stabilising the network.
- 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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