Project management internships

Supervisor: Dr Shahadat Uddin 

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: This project aims to investigate and mitigate biases in LLMs by developing a machine learning pipeline that detects and reduces hallucinations and biased outputs in AI-generated responses. First, it will collect and preprocess datasets containing AI-generated outputs across different demographic groups. It will then apply sentiment analysis and fairness metrics (e.g., Demographic Parity, Equalized Odds) to identify bias.

Next, it will apply adversarial training and reinforcement learning to mitigate bias. Finally, the project will evaluate the ethical impact of different mitigation strategies. The project outcomes will help ensure fairness and ethical AI use in real-world applications like healthcare, hiring, and education. 

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

Supervisors: Associate Professor Michele Barnes, Dr Henry Bartelet 

Eligibility:  

• Student is at least in year two of their undergraduate program [required] 
• Proficiency in English [required] 
• Prior experience with creating or working with (project) databases in Microsoft Excel [required] 
• Artificial Intelligence skills to create a tool to scan project documents automatically and extract relevant information [optional] 

Project Description: Climate adaptation projects play a critical role in supporting community resilience—but how these projects are designed, and whether they address the full range of community vulnerabilities in an equitable manner, remains unclear. Recent research shows that human adaptation depends not only on “hard” inputs such as the assets that people can draw on, but also on “soft” capacities such as social networks, agency/power, and learning. Yet historically adaptation projects have focused heavily on building assets and infrastructure. 

This project creates a structured database of implemented adaptation projects funded through such mechanisms as the Adaptation Fund and UNDP’s Climate Fund, analyzing where they were implemented, how they were designed, what capacities they aimed to build, and who was involved in planning. This will help identify common design patterns and gaps in current practice.  

The intern will assist with reviewing and coding project documentation, gaining practical skills in project evaluation, data management, and critical analysis—valuable experience for students interested in project management, climate adaptation, and global development. 

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

Supervisor: Dr Sujuan Zhang 

Eligibility:  

• WAM above 80  
• Passion for knowing, eager to learn  
• Basic knowledge about sustainable infrastructure
• Basic skills of doing a literature review, case study or policy analysis 

Project Description: From the project perspective, projects can be seen as vehicles to bring about socio-technical systems or sustainable transitions to address grand challenges such as sustainability and climate change. This is also considered as sustainability by projects: how can projects be organized to contribute to bigger sustainable transitions? To answer the question, we are looking at the case of renewable energy zones in NSW. 

Australia’s National Electricity Market (NEM) is transitioning to a future where the majority of electricity generated is through renewable sources such as wind and solar. Across the NEM, Renewable Energy Zones (REZs) are where clusters of large-scale renewable energy projects can be developed using economies of scale, supported by network infrastructure (such as Central West Orana and the Waratah Super Battery in NSW). A case study will be used to address the question. 

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

Supervisor: Dr Xinyue Zhang 

Eligibility: Proficiency in reading and writing Chinese characters to analyse raw data from case materials. 

Project Description: The dual nature of major infrastructure projects as both public goods and commodities necessitates a unique approach to their governance. These projects are critical for providing fundamental public services and supporting societal and economic development. Simultaneously, they operate within a market-driven environment where resource allocation and stakeholder interactions are guided by market principles. This duality poses significant challenges for project governance, which must extend beyond traditional project management practices to encompass broader governance domains influenced by both government and market forces.

This project aims to explore the governance model of major infrastructure projects, investigating the combined influence of government and market forces. The research will adopt multiple case studies, examining some prominent projects worldwide. The findings are expected to reveal the intricate dynamics of government and market participation across different organisational levels and phases of major infrastructure projects. The successful candidate will conduct data collection and analysis of case materials, and draw organisational design models of infrastructure projects. 

Requirement to be on campus: No 

Supervisor: Dr Jin Xue 

Eligibility:  

• Basic proficiency in spatial analysis tools and Python coding. 
• Basic knowledge of AI tools, such as ChatGPT, LLaMA, or Gemini. 
• Hold a Remote Pilot Licence (RePL) for drone operating preferred 
• Experience with web scraping techniques is advantageous. 

Project Description: Australia’s accelerating renewable energy expansion, notably in New South Wales (NSW), fuels wind and solar projects to achieve net-zero targets. However, this increase threatens biodiversity, endangering habitats and displacing species in natural regions. 

This project will advance biodiversity assessment by deploying a novel framework. It requires students to join the project team in operating drones at select NSW sites, collecting geospatial data to overcome the limitations of traditional field surveys and manual evaluations, while synthesizing diverse data types—spatial patterns and textual commitments—for scalable analysis. 

The approach processes this drone-acquired remote sensing data with GIS tools like ArcGIS to map ecological risks. Concurrently, large language models (LLMs) extract and analyze biodiversity pledges from project documents, enabling a comprehensive risk-commitment comparison. 

Completion will empower students with hands-on expertise in drone-based data acquisition and AI analysis, linking theory to practice and cultivating interdisciplinary insights across data science, environmental management, and innovative technology. 

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

Supervisor: Dr Hoonyong Lee 

Eligibility: 

• Demonstrated interest in remote work, team dynamics, or human-centered technologies. 
• Willingness to work with wearable sensors and basic data analysis (training will be provided). 
• Strong communication and collaboration skills. 

Project Description: This project aims to mitigate social disconnection in remote work by strengthening interpersonal bonds. It develops a continuous monitoring framework that identifies the early stage of social disconnection for timely interventions.

This framework detects weakening interpersonal bonds by monitoring the physiological responses of remote team members using wearable biosensors. Then, it delivers personalised messaging feedback to enhance proactive engagement and team cohesion, thereby restoring interpersonal bonds and mitigating social disconnection before it fully develops.

Project outcomes will improve workplace resilience and team productivity, contributing to the sustainability and economic benefits of Australia’s remote work culture. 

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

Supervisors: Professor Jennifer Whyte, Dr Wei-Ting Hong 

Eligibility: Candidates with the following skills are preferred. 

• Familiar with natural language processing, prompt engineering, large language models, and Python 
• Skills in project management, infrastructure projects, project models, or digital transformation 

Project Description: This project investigates how to leverage emerging technologies and data-driven approaches to achieve efficient expert-AI collaboration and support complex project delivery, enhancing the efficiency of information exchange across various sectors. Specifically, this project aims to use novel technologies, such as Large Language Models (LLMs) and advanced prompt engineering (prior experience with these techniques is desired, not mandatory; successful applicants will have the opportunity to learn and develop these skills as part of the project), to standardise data from different stakeholders in a complex project, such as infrastructure management and building a prototype of a decision-making support system. 

You are also expected to bridge the gap between technology and industry practice. You might have opportunities to pitch this system to industry partners. The outputs from this internship program include one scientific paper on enabling AI in project decision-making and a presentation detailing the findings and implications for future project delivery models.  

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