Civil engineering internships

Supervisor: Dr Jiaying Li

Eligibility: This project is open to applications from students with broad background in engineering and science discipline.  WAM>75 and Undergraduate candidates must have already completed at least 72 credit points towards their undergraduate degree at the time of application.

Project Description:

Effective removal of emerging contaminants (ECs) in water is challenging due to their stable chemical structures. Plasma electrochemical technique shows a promising potential to destruct complex chemicals by triggering radical and electrochemical reactions across gas-liquid interfaces at room temperature. In this project, we will conduct the destruction experiments based on the laboratory-scale plasma reactors to evaluate the removal efficiencies of several ECs in water. The project will be conducted in the laboratory with chemical experiments and analysis required. Students may be asked to produce a report or presentation summarizing their work at the end of the project.

Requirement to be on campus:Yes *dependent on government’s health advice.

Supervisor: Dr Jiaying Li

Eligibility: This project is open to applications from students with a background in broad engineering and science discipline.  WAM>75 and Undergraduate candidates must have already completed at least 72 credit points towards their undergraduate degree at the time of application.

Project Description:

Emerging contaminants like pose an increasing threat to human and environmental health. Given their widespread presence in the environment, there is an urgent need for rapid, practical, and cost-effective methods to detect ECs in water environments. In this project, we will design and develop simple and inexpensive sensing methods for chemicals. In this project, students will gain hands-on experience in designing and developing novel sensors for detecting chemicals in water samples. The project will be conducted in the laboratory with chemical experiments and analysis required. Students may be asked to produce a report or presentation summarizing their work at the end of the project.

Requirement to be on campus: Yes *dependent on government’s health advice

Supervisor: Dr Jiaying Li

Eligibility: This project is open to applications from students with broad background in engineering and science discipline, , ideally with relevant skills in GIS and R or Python.  WAM>75 and Undergraduate candidates must have already completed at least 72 credit points towards their undergraduate degree at the time of application.

Project Description:

Chemical exposure serves as an important indicator of public health and wellbeing. In this project, we will link chemical contaminant data to population socioeconomics using QGIS or ArcGIS, and identify the drivers of changes. In this project, students will gain hands-on experience in data collection, data processing, GIS mapping, and multivariable analysis. The project will be conducted on-site, with desktop work using GIS software required. Students may be asked to produce a report or presentation summarizing their work at the end of the project.

Requirement to be on campus: No

Supervisor: Prof. David Levinson

Eligibility: WAM>75 and Undergraduate candidates must have already completed at least 72 credit points towards their undergraduate degree at the time of application

Project Description:

How busy are city intersections? Usually, we count pedestrians with surveys or cameras, but traffic signals already record useful clues. This project explores how to use those records to estimate pedestrian numbers. The idea is simple: when the “Flashing Don’t Walk” sign starts, the time until the first person presses the crossing button tells us something about how often people are arriving. By repeating this across many cycles, we can build a picture of pedestrian flows at different times of day.

The work will involve three main steps. First, collecting data from observations of intersections in Sydney. Second, building a model that connects button press timing to actual pedestrian arrivals. Third, comparing the model results with observed counts to see how well it works. Students will gain experience in data analysis, modelling, and validation, while tackling a real-world urban problem.

Requirement to be on campus: No

Supervisor: Dr Faham Tahmasebinia

Eligibility: WAM>75 and Undergraduate candidates must have already completed at least 72 credit points towards their undergraduate degree at the time of application

Project Description:

Implementing Artificial Intelligence (AI) techniques in the enhancement of steel moment frame structures signifies a groundbreaking shift in how these essential engineering systems are designed, analysed, and optimized. This review covers a broad array of AI strategies, such as machine learning algorithms, evolutionary algorithms, neural networks, and advanced optimization methods, which are utilized to tackle various challenges within the sector. The consolidation of these research findings underscores the interdisciplinary approach of AI in structural engineering, highlighting the integration of domain expertise with sophisticated computational methods. This comprehensive synthesis is a crucial resource for researchers, practitioners, and policymakers looking to grasp the cutting-edge developments in AI-enabled optimization of steel moment frame structures.

References: Mohsen Soori, Fooad Karimi Ghaleh Jough. Artificial Intelligent in Optimization of Steel Moment Frame Structures: A Review. International Journal of Structural and Construction Engineering, 2024.

Requirement to be on campus: No

Supervisor: Dr Faham Tahmasebinia

Eligibility: WAM>75 and Undergraduate candidates must have already completed at least 72 credit points towards their undergraduate degree at the time of application.

Project Description:

Artificial intelligence encompasses a range of techniques and fields, such as vision, perception, speech and dialogue, decision-making, planning, problem-solving, robotics, and other areas conducive to autonomous learning. This study focuses on exploring the potential of AI algorithms to enhance safety throughout different phases of the construction process. The research reviewed the scientific literature on applying artificial intelligence in construction and optimising these processes.

References: https://www.mdpi.com/1424-8220/23/21/8740

Requirement to be on campus: No

Supervisor: Emily Nabong

Eligibility: WAM>75 and Undergraduate candidates must have already completed at least 72 credit points towards their undergraduate degree at the time of application.

Project Description:

As the impacts of climate change intensify, many communities are questioning whether they can remain where they are and what options exist to adapt. At the same time, international development agencies are reassessing the costs and benefits of investing in infrastructure for regions that may ultimately become uninhabitable. Recent cuts to global aid budgets make these questions even more urgent.

This project will investigate how shifts in development assistance may unintentionally contribute to premature environmental degradation or destabilising societal change. The research student will review literature on the topic, apply systems thinking methodologies, and use system mapping tools (eg. Kumu) to visualise feedbacks, trade-offs, and unintended consequences within these dynamics.

Requirement to be on campus: No

Supervisor: Emily Nabong

Eligibility: WAM>75 and Undergraduate candidates must have already completed at least 72 credit points towards their undergraduate degree at the time of application.

Project Description:

As Australia pursues net-zero emissions targets, new technologies will reshape relationships between food, energy, and water systems. While new energy solutions aim to address climate challenges, they may also create unintended pressures in other sectors. From a systems view, we can evaluate both the intended benefits and the potential downstream consequences of these choices.

This project explores how new energy technologies affect the food-energy-water nexus in Australia. The student will assist in reviewing new technologies and proposed policies, developing

scenarios, and integrating these into a system dynamics model. The student is expected to analyse the results of the modelled scenario(s) and produce a summary of their findings. Students with prior experience in system dynamics modelling are preferred.

Requirement to be on campus: No