UQ Winter Research Program
The UQ Winter Research Program provides the chance to road-test research alongside UQ academics and researchers.
The program enables you to extend your knowledge of an area of interest, and to develop your analytical, critical thinking and communication skills.
The Research Experience Programs are open to undergraduate (including honours) and postgraduate coursework students studying at The University of Queensland.
Find your project (Projects available in 2025)
See the list of our available projects available from the School of the Environment below.
Advanced methods in determining the age of Earth materials and their applications to critical minerals research
Hours of Engagement:
36 Hours
Location:
St Lucia
Project Description:
Geochronology, the science of determining the age of minerals or rocks, is at the core of geoscience. Students will work with the state-of-the-art facilities at the School of the Environment and contribute to developing in-house protocols on one of novel applications in geochronology with laser ablation, scanning electron microscope, and inductively coupled plasma mass spectrometry. Students could then apply these methods in projects in understanding the development of critical minerals, age-dating paleoenvironment events and fossils, and the growth of mountain systems and tectonic plate margins. Please get in touch with the supervisor for additional information.
Possible Positions:
2
Suitable Applicants:
Undergraduate students who are interested in general topics in Earth, environmental, and marine sciences are encouraged to apply. Candidates who have completed ERTH1000, ERTH1501, GEOS1100, MARS1001, or one or more second-year ERTH and MARS courses would be given priority.
Supervisor:
AI-Driven Digital Solutions for Advancing Circular Economy in Green Hydrogen Production in Australia
Hours of Engagement:
36 Hours
Location:
St Lucia
Project Description:
This 4-week research project aims to explore the integration of artificial intelligence (AI) and digital tools to enhance the efficiency and sustainability of green hydrogen production in Australia. By aligning with circular economy principles, this project seeks to address key challenges in scaling up green hydrogen systems, bridging disciplines such as engineering, business, and environmental science.
Possible Positions:
1
Suitable Applicants:
Students from the following study areas are suitable applicants for this research project:
- Environmental and Sustainability Disciplines:
- Environmental Science/Management
- Sustainability Science
- Circular Economy
- Engineering Disciplines:
- Mechanical Engineering
- Chemical Engineering
- Environmental Engineering
- Computer Science and AI Disciplines:
- Computer Science
- Data Science
- Artificial Intelligence and Machine Learning
- Digital Engineering
- Business and Economics Disciplines:
- Business Analytics
- Environmental Economics
- Sustainable Business
Prior Experience and Skill Requirements
While prior experience in all areas is not mandatory, students should ideally have the following:
- Technical and Computational Skills:
- Familiarity with AI/ML tools (e.g., Python, TensorFlow, Scikit-Learn)
- Basic knowledge of digital simulation tools (e.g., MATLAB, Simulink, Aspen Plus, COMSOL, or equivalent)
- Experience in programming and data analytics (e.g., R, Python, or SQL)
- Knowledge of Green Hydrogen and Circular Economy:
- Understanding of hydrogen production technologies (especially electrolysis)
- Exposure to life cycle assessment (LCA) or sustainability assessment methods
- Awareness of circular economy principles and industrial decarbonization
- Research and Analytical Skills:
- Ability to conduct literature reviews and synthesize findings
- Strong problem-solving skills for interdisciplinary challenges
- Data analysis and visualization experience
- Interdisciplinary and Collaborative Skills:
- Interest in sustainability and energy systems
- Ability to work in interdisciplinary teams (engineering, business, environmental science)
- Effective communication skills for presenting findings
Prerequisite Courses (Preferred but Not Mandatory)
Students who have completed or are currently enrolled in the following courses will have an advantage:
- For Engineering Students:
- Energy Systems and Renewable Energy Technologies
- Process Modelling and Simulation
- Digital Twins and AI Applications in Engineering
- For Computer Science & Data Science Students:
- Machine Learning and AI for Engineering Applications
- Data Analytics for Sustainability
- Optimization Techniques
- For Environmental Science & Circular Economy Students:
- Life Cycle Assessment and Environmental Footprinting
- Sustainable Business Practices
- Circular Economy Strategies
- For Business & Economics Students:
- Green Finance and Sustainable Investment
- Supply Chain Analytics and Circular Business Models
- Digital Transformation for Sustainable Industries
Ideal Candidate Profile:
- Senior undergraduate (third or fourth year) or master’s student
- Demonstrated interest in AI, digital solutions, or sustainability
- Background in at least one of the mentioned disciplines
- Ability to apply theoretical knowledge to real-world applications
- Willingness to engage in interdisciplinary research
Supervisor:
Environmental Life Cycle Assessment of Circular and Renewable Construction Materials
Hours of Engagement:
36 Hours
Location:
St Lucia
Project Description:
This project will evaluate the environmental impacts of circular and renewable construction materials using life cycle assessment (LCA) method. Over four weeks, the research will focus on identifying key materials, analysing their production and end-of-life processes, and quantifying their environmental performance across the life cycle stages. The study aims to provide actionable insights into how these materials can reduce carbon footprints, enhance resource efficiency, and support the transition to sustainable construction practices. Anticipated outcomes include a detailed LCA report and recommendations for integrating circular and renewable materials into building projects.
Possible Positions:
1
Suitable Applicants:
Eligible Study Areas:
Students from the following programs will be suitable for this project:
- Environmental Science/Management/Engineering
- Civil and Structural Engineering (with an interest in sustainable materials)
- Materials Science and Engineering
- Industrial Ecology
- Architecture (Sustainable Design Track)
- Construction Management (with a focus on sustainability)
Prior Experience & Skills Required:
- Basic knowledge of Life Cycle Assessment (LCA) principles, methods, and software (e.g., SimaPro, GaBi, OpenLCA) is preferred.
- Understanding of circular economy principles and sustainable material design in construction.
- Experience with data analysis and environmental impact assessment using tools such as Excel, Python, R, or MATLAB would be beneficial.
- Strong research and analytical skills for reviewing literature on construction materials, waste management, and material flows.
- Ability to interpret and apply LCA methodologies to real-world case studies.
- Interest in carbon footprint reduction, resource efficiency, and eco-friendly construction solutions.
Prerequisite Courses (Preferred but not Mandatory):
Students should ideally have completed at least one of the following courses before applying:
- Life Cycle Assessment and Sustainability Analysis
- Sustainable Building Materials & Construction
- Environmental Impact Assessment
- Circular Economy and Resource Efficiency
- Green Engineering and Sustainable Design
- Materials Science with a Focus on Renewable & Recycled Materials
- Environmental Systems Modelling
Ideal Candidate Profile:
- Senior Undergraduate Students: Must have completed at least 3 years of study with relevant coursework in LCA, sustainability, or materials science.
- Master’s Students: Preferred if they are specializing in sustainable construction, environmental systems, or LCA methodologies. This project is ideal for students who want to gain hands-on experience in applying LCA to real-world construction materials and contribute to sustainability research that supports circular economy transitions.
Supervisor:
Detection of Wolbachia in mosquitoes using smart-phone operated near-infrared spectrometer (SCiO)
Hours of Engagement:
For the Winter program, students will be engaged for 4 weeks only.
20 – 36 hrs per week between 30 June – 25 July 2025).
Location:
St Lucia
Project Description:
The Lord Laboratory is developing next-generation tools to predict mosquito parameters that are crucial for disease transmission including age, infection status and species identity. This winter project will determine the capacity of a smart-phone operated near-infrared spectrometer (NIRS) known as SCiO to predict whether mosquitoes are carrying Wolbachia, an endosymbiont bacteria that blocks pathogen transmission in mosquitoes. The results will be compared with a benchtop instrument Labspec 4i. The technique involves shining a beam of near-infrared light on mosquitoes, collecting a spectrum and analysing the spectrum to predict the presence/absence of infection. The project will involve rearing mosquitoes for approximately 2 weeks, scanning them for 1 week and writing a short report for 1 week.
Scholars will gain skills in mosquito rearing, infrared analysis and development of simple machine learning algorithms to predict presence or absence of infection. Students will also be asked to produce a short report at the end of their project.
The supervisor wishes to be contacted by students prior to submitting an application.
Suitable Applicants:
This project is open to applications from students with a background in Biology
Supervisor:
Detection of Wolbachia in mosquitoes using smart-phone operated near-infrared spectrometer (NIRvascan)
Hours of Engagement:
For the Winter program, students will be engaged for 4 weeks only.
20 – 36 hrs per week between 30 June – 25 July 2025).
Location:
St Lucia
Project Description:
The Lord Laboratory is developing next-generation tools to predict mosquito parameters that are crucial for disease transmission including age, infection status and species identity. This winter project will determine the capacity of a smart-phone operated near-infrared spectrometer (NIRS) known as NIRvascan to predict whether mosquitoes are carrying Wolbachia, an endosymbiont bacteria that blocks pathogen transmission in mosquitoes. The results will be compared with a benchtop instrument Labspec 4i. The technique involves shining a beam of near-infrared light on mosquitoes, collecting a spectrum and analysing the spectrum to predict the presence/absence of infection. The project will involve rearing mosquitoes for approximately 2 weeks, scanning them for 1 week and writing a short report for 1 week..
Scholars will gain skills in mosquito rearing, infrared analysis and development of simple machine learning algorithms to predict presence or absence of infection. Students will also be asked to produce a short report at the end of their project.
The supervisor wishes to be contacted by students prior to submitting an application.
Suitable Applicants:
This project is open to applications from students with a background in Biology
Supervisor:
Application details
Find out more about the UQ Winter Research Program, including eligibility guidelines and application details.