Civil engineering internships

Supervisor: A/Prof Ali Hadigheh

Eligibility: WAM>75. Students applying for projects within the School of Civil Engineering must    have completed at least 72 credit points at the time of application.

Project Description:

This project focuses on the development of advanced cementitious composite materials that are both multifunctional and environmentally sustainable. By integrating industrial waste, recycled materials, and innovative additives, the research aims to enhance performance, durability, and additional functionalities such as self-sensing, corrosion resistance, and energy harvesting. The project combines experimental work with material characterisation techniques and microbial technologies to explore novel pathways for sustainable construction. It is well-suited for students interested in materials science, civil engineering, sustainability, and interdisciplinary research with industry relevance.

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.  Students applying for projects within the School of Civil Engineering must have completed at least 72 credit points 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: WAM>75.  Students applying for projects within the School of Chemical and Biomolecular Engineering must have completed at least 72 credit points at the time of application.

Project Description:

There is an emerging need for easy-to-use methods of testing the susceptibility of bacteria that form biofilms and for screening new possible antibiofilm strategies. In this project, we will design and develop a miniaturised biofilm-on-a-chip platform, enabling reduced water usage while preserving biofilm structure and stratification. In this project, students will gain hands-on experience in designing and developing mini reactors/chips to investigate biofilm responses to chemical and fluid shear stress. The project will be conducted based on laptop/computer (e.g. using CAD software). 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.

Supervisors: Dr Viraj Vidura Herath Herath Mudiyanselage, Prof. Lucy Marshall

Eligibility: 

• Interest in flood modelling, machine learning, or scientific computing
• Willingness to learn TUFLOW and Python-based data processing
• Careful and organised approach to working with simulation datasets
• Prior experience with Python, GIS, hydrodynamic modelling, or machine learning is helpful but not required
• WAM>75.  Students applying for projects within the School of Civil Engineering must have completed at least 72 credit points at the time of application.

Project Description:

Physics-based hydrodynamic models provide reliable flood simulations, but their high computational cost limits rapid forecasting and large-scale analysis. Machine-learning surrogate models can accelerate prediction; however, existing methods are rarely compared on a common benchmark or evaluated using calibrated, real-world flood models.

To address this gap, our team, in collaboration with the National University of Singapore, released UrbanFloodBench V1, launched a global Kaggle competition that attracted around 4,000 submissions, and prepared a research paper currently under review. Building on this work, the project will develop UrbanFloodBench V2 using calibrated Australian flood models developed in TUFLOW.

Interns will curate hydrodynamic simulations, undertake quality checks, and convert model inputs and outputs into consistent, machine-learning-ready datasets. The project offers hands-on experience in flood modelling, scientific data engineering, and AI benchmarking. Students making substantial contributions may be invited to co-author publications and continue collaborating with the international research team.

Requirement to be on campus: No. The project can be completed remotely, with regular online meetings.

Supervisor: Dr Faham Tahmasebinia

Eligibility: WAM>75.  Students applying for projects within the School of Civil Engineering must have completed at least 72 credit points 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.  Students applying for projects within the School of Civil Engineering must have completed at least 72 credit points 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: Sooyeol (Suzy) Kim 

Eligibility:

• This project is open to applications from students with broad background in engineering and science discipline. Applicants should have basic coding skills.
• WAM>75.  Students applying for projects within the School of Chemical and Biomolecular Engineering must have completed at least 72 credit points at the time of application.

Project Description:

As urban water systems age, rainwater and groundwater frequently leak into closed sanitary sewers, a costly engineering challenge known as inflow and infiltration (I&I). Unmanaged I&I overburdens wastewater treatment facilities, triggers sewage overflows, and can lead to pathogen exposure and environmental contamination for the surrounding communities. Detecting these hidden leaks traditionally requires expensive, invasive physical pipe inspections. In this project, we will explore how environmental monitoring data can serve as a non-invasive diagnostic tool to identify failing pipe networks across major cities. Using time-series datasets and weather patterns, students will gain experience with data wrangling, statistical analysis, and data visualization, producing insights for urban water resilience and gaining transferable data science skills applicable to any field working with complex, real-world data.

Requirement to be on campus: No

Supervisor: Sooyeol (Suzy) Kim 

Eligibility: 

• This project is open to applications from students with broad background in engineering and science discipline. Applicants should have basic coding skills and familiarity with command-line tools. Previous experience of bioinformatics is preferred. 
• WAM>75.  Students applying for projects within the School of Chemical and Biomolecular Engineering must have completed at least 72 credit points at the time of application.

Project Description:

Wastewater-based epidemiology provides non-invasive, population-scale insights into public health of communities. Since the pandemic, researchers around the world have used next-generation sequencing methods on wastewater samples to generate vast metagenomic datasets that allow exploration of global pathogen dissemination and transmission. This high-throughput sequencing surge creates an exciting opportunity to mine big data for hidden microbial dynamics. Leveraging publicly available datasets, this project investigates pathogen diversity and strain sharing across global sewage and fecal metagenomes. Students will learn to work with sequencing data, run bioinformatic pipelines, and perform phylogenetic analyses on target pathogens, gaining scalable data science skills applicable across modern biological fields.

Requirement to be on campus: No

Supervisor: Sooyeol (Suzy) Kim 

Eligibility:

• This project is open to applications from students with broad background in engineering and science discipline. Applicants should have basic coding skills. Previous lab experience in molecular biology is preferred. 
• WAM>75.  Students applying for projects within the School of Chemical and Biomolecular Engineering must have completed at least 72 credit points at the time of application.

Project Description:

Mobile genetic elements, or the “mobilome,” drive the rapid spread of antimicrobial resistance and virulence traits across environmental microbial communities. However, existing sequencing strategies struggle to isolate these low-abundance, dynamic genetic structures from complex background DNA. In this project, we will generate proof-of-concept data to test a novel targeted enrichment strategy to selectively isolate mobile genetic elements from complex environmental samples. Students will learn to mine databases to design molecular assays, conduct in-silico analysis to evaluate their chosen target, and validate the created molecular assays in lab, gaining both computational and lab skills while contributing foundational data to next-generation sequencing frameworks.  

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

Supervisors: A/Prof Daniel Dias-da-Costa, Donald Proctor

Eligibility: WAM≥75 and Undergraduate candidates must have already completed at least 96 credit points towards their undergraduate degree at the time of application.

Project Description:

Architecture & Planning alumni Donald Proctor is looking for enthusiasts to join his quest to design and build a dome that is made of tetrahedra linked to form integrated trusses that form an interesting dome.

Potential learning experiences would include:

• Literature search looking at dome construction, geodesics, tetrahelix, geometric modelling.
• Computer 3D modelling.
• 3D printing (of connecting nodes)
• Project management
• Practical modular building solutions  

The starting vision is to create prototypes leading to submission to the 2027 Sculptures by the Sea exhibition. Also, throughout the project we will explore further potential applications, perhaps using the Buckminster fuller geodesic dome built for Montreal Expo 67 as inspiration.

This project is envisaged as a vehicle for ideas, creativity, hands on building, teamwork and self determined investigation and discovery.

Requirement to be on campus: No

Supervisor: Prof Anna Paradowska 

Eligibility: WAM>75.  Students applying for projects within the School of Chemical and Biomolecular Engineering must have completed at least 72 credit points at the time of application.

Project Description:

This project establishes the technical foundation for transforming how metals are recovered, upgraded, and reintroduced into high-value manufacturing. Rather than treating metal scrap as a low-value commodity, it demonstrates its potential as a qualified feedstock for Additive Friction Stir Deposition (AFSD). By converting scrap into high-quality manufacturing inputs, the project redefines waste as a strategic materials resource and supports the development of an integrated recycling and manufacturing ecosystem. This approach will improve resource efficiency, reduce reliance on virgin materials, lower embodied carbon emissions, and strengthen sovereign manufacturing capability. The project will enable industry to produce high-performance components from recycled feedstocks without compromising quality or performance, supporting the transition to a circular metals economy. The student will assist with initial manufacturing trials and receive training in data processing, analysis, and optimisation. Working with a range of metal scrap materials, the student will gain valuable skills in sustainable manufacturing, materials engineering, and circular economy practices.

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