Project management internships

Supervisor: Dr Hoonyong Lee

Eligibility: 

    -    Interest in data analysis, smart sensing, or AI

    -    Basic programming skills (Python or similar)

    -     Curiosity about smart environments and sensing technologies

    -     No prior research experience is required.

Project Description:

Monitoring crowd density is important for improving safety, comfort, and operational management in public environments such as transport hubs, events, and large buildings. Traditional crowd monitoring methods often rely on cameras or manual counting, which may raise privacy concerns or require significant infrastructure.

Recent studies suggest that wireless signals such as WiFi can provide an alternative sensing approach for estimating crowd density. The presence and movement of people influence WiFi signal characteristics, making it possible to infer crowd levels through signal analysis.

This project explores the feasibility of using WiFi-based sensing to estimate crowd density in outdoor environments. The student will investigate how wireless signal data can be used to infer the presence and concentration of people, and explore simple machine learning or statistical methods for estimating crowd levels.

The project will provide hands-on experience in sensing technologies, data analysis, and smart environment monitoring.

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

Supervisors: A/Prof Ken Chung

Eligibility: Distinction average, interviewing skills, qualitative data analysis.

Project Description:

The majority of today’s project professionals did not set out to become project managers—they arrived at their roles through unplanned pathways. In response to this trend, significant

investments have been made across industry and academia to support the intentional development of project leadership capabilities.  This research project investigates the career trajectories of graduates from the Bachelor of Project Management (BPM) program. The study aims to understand how these individuals have navigated their professional journey, including

their experiences within industry, the challenges they have encountered, and the dilemmas they have faced.  This experience will be particularly valuable for students with interests in project management, leadership, organisational studies, or social research, and may serve as a foundation for future honours or postgraduate research.

Requirement to be on campus: No

Supervisors: A/Prof Ken Chung

Eligibility: Distinction average. Desirable competencies: data analytics, mixed methods study, computing and quantitative analysis such as Python.

Project Description:

Community stakeholders now wield significant influence over projects through social media, where public sentiment—especially negative—can rapidly spread and even halt project progress. Misinformation and distrust are easily amplified, often outpacing the ability of project authorities to respond effectively. With the rise of AI-generated content, including synthetic voice, video, and deepfakes, the challenge of managing stakeholder perceptions has become more urgent and complex.

This research investigates how social media and AI technologies shape stakeholder engagement and influence project outcomes. It aims to understand the mechanisms behind the spread of distrust and misinformation, and to explore how project leaders can navigate this evolving landscape. The ultimate goal is to develop a strategic framework for managing digital stakeholder dynamics and to propose practical approaches for fostering trust, countering misinformation, and enhancing engagement in the age of AI.

Requirement to be on campus: No

Supervisor: Dr Hoonyong Lee

Eligibility: Interest in virtual reality, safety, or human-centred design.  Curiosity about construction safety or workplace training.  Basic familiarity with digital tools (VR experience not required).

Project Description:

Construction and industrial workplaces often involve workers from diverse linguistic and cultural backgrounds. Traditional safety training frequently relies on written instructions or verbal explanations, which may not be equally effective for workers with limited language proficiency.

Virtual Reality (VR) offers a promising platform for immersive and experiential safety training. By using visual scenarios, interactive environments, and spatial demonstrations, VR may enable workers to understand safety procedures without relying heavily on language.

This project explores how VR can support language-free safety training, focusing on visual and experiential communication of safety concepts. The student will investigate how safety hazards and procedures can be communicated using visual cues, environmental context, and interactive VR scenarios.

The project will contribute to the development of more inclusive safety training approaches for multicultural workforces.

Requirement to be on campus: Yes – preferred but flexible*dependent on government’s health advice

Supervisors: Dr. Jin Xue

Eligibility:

    -    Strong interest in environmental values, sustainability, or the intersection of technology           and society.

    -    Basic proficiency in NLP and Python coding.

    -    Strong interest in learning and applying latest AI techniques, such as Multi-

    -    Modal analysis, Agentic AI, etc.

Project Description:

In the face of increasing environmental challenges, diverse environmental values shape public opinion-from human-centred economic priorities to nature conservation. Understanding these value systems is crucial for developing acceptable and effective sustainability policies.

This project investigates how environmental narratives are constructed and propagated on digital platforms, using social media data as a window into public values, conflicts, and sentiment dynamics. How can multi-modal AI models, especially large language models (LLMs), reveal patterns in environmental discourse and stakeholder salience?

The study will apply natural language processing and image-text alignment techniques to extract themes, sentiments, and influential voices from multi-platform social media data. Students will assist in data collection, fine-tuning LLMs, and discourse analysis.

Completion will empower students with hands-on expertise in social media 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 government’s health advice

Supervisor: Dr Jin Xue

Eligibility:

    -    Basic proficiency in NLP and Python coding.

    -    Basic knowledge of AI tools, such as ChatGPT, Claude, or Gemini.

    -    Experience with RAG and AI-agent techniques is advantageous.

Project Description:

This project investigates how AI-enabled knowledge management systems can strengthen data-driven decision-making (DDDM) across major project, portfolio, and program environments in Australia. Major projects often face significant cost and schedule overruns due to complex decision-making with imperfect data.

The intern will assist in an LLM-driven research approach, including:

(1) Conducting a systematic literature review on DDDM and AI tools.

(2) Designing Prompts and Skills for dealing with various kinds of imperfect data.

(3) Building an AI-driven pipeline to integrate the fragmented information for decision-making.

The goal is to develop an AI-enhanced DDDM framework that improves decision quality and delivery assurance by integrating fragmented information and enabling learning across projects.

Through this project, the student will develop practical expertise in AI-enabled knowledge management tailored for complex data-driven decision-making. The participant will gain hands-on experience in building AI-driven pipelines and applying RAG or AI-agent techniques to resolve information fragmentation in major projects.

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

Supervisor: Dr Jin Xue

Eligibility:

    -    Basic proficiency in NLP and Python coding.

    -    Basic knowledge of AI tools, such as ChatGPT, Claude, or Gemini.

    -    Experience with RAG and AI-agent techniques is advantageous.

Project Description:

This project explores the Large Language Model-based framework to address the "Contextual Blind Spot" in modern project-based organizations. While current systems track "what" happened, the critical "why" behind decisions is often lost when experts leave. The intern will research the "Discourses" framework, which captures unstructured human interactions as structured "Conversation-as-Data". Key tasks include:

(1)    Investigating the development of Contextual Knowledge Graphs (CKG) based on human conversations.

(2)    Developing the Reinforcement Learning from Human Feedback (RLHF) mechanism for smart project decision making.

(3)    Creating the “human-in-the-loop” AI framework for future project decision making.

This research aims to move organizations toward an "emergent intelligence" that learns and evolves over time.

Through this internship, the student will gain hands-on experience in developing Contextual Knowledge Graphs (CKG) and implementing Reinforcement Learning from Human Feedback (RLHF) mechanisms for decision-making within a "human-in-the-loop" AI framework.

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

Supervisors: Xuanqi Li and Dr. Sujuan Zhang

Eligibility: Distinction average.  Desirable competencies: data analytics, familiarity with NLP and LLM-based text analysis.

Project Description:

The global transition toward low-carbon energy systems is increasingly driven by public policies designed to accelerate technological change, infrastructure development, and investment in renewable energy. These policies play a critical role in shaping how energy transition projects emerge and are implemented. However, translating policy ambitions into effective projects often involves complex interactions among policy frameworks, institutions, and project actors.

This project aims to investigate how energy transition policies influence the development and implementation of energy projects across different sectors in Australia. The research will analyse energy transition policy documents from international databases (e.g., IEA policy databases) to identify patterns in policy design and implementation.

The intern will assist with collecting and organising policy documents, conducting literature and policy reviews, supporting policy coding and basic text analysis, and helping structure datasets for further analysis of energy transition policies.

Requirement to be on campus: No

Supervisor: Dr. Sujuan Zhang

Eligibility: Distinction average.  Desirable competencies: data analytics, familiarity with qualitative data coding and thematic analysis of interview data

Project Description:

The global transition to low-carbon energy systems requires the rapid delivery of complex infrastructure projects, including renewable energy, grid upgrades, and storage systems. However, many energy transition projects face significant delivery challenges, including regulatory uncertainty, governance fragmentation, stakeholder conflicts, and capability gaps across organisations.

This research project aims to investigate the barriers and governance gaps affecting the delivery of energy transition projects. Building on an ongoing research project that has already conducted a series of industry interviews with key stakeholders involved in energy infrastructure delivery, the study seeks to deepen understanding of how organisations navigate these challenges and what capabilities are required to support effective transition.

The student researcher will contribute to the project by assisting with literature review, interview data collection, and thematic analysis of interview data. There may also be opportunities to support the design and conduct of additional interviews with practitioners involved in energy transition projects.

The findings will contribute to emerging research on project organising for sustainability transitions and provide practical insights for policymakers, project owners, and infrastructure organisations.

Requirement to be on campus: No

Supervisor: Dr Neda Mohammadi

Eligibility:

    -    Strong programming skills, preferably in Python Interest in systems modelling,            uncertainty, and decision support

    -    Curiosity about emerging quantum methods and hybrid quantum–classical approaches

    -    Ability to analyse, visualise, and communicate results clearly

Project Description:

Complex systems rarely fail or succeed because of one decision alone. Their outcomes emerge through chains of choices, responses, and changing conditions over time. This project explores how a quantum digital twin can support scenario modelling by representing a system, its uncertainties, and the decision points that shape future outcomes. You will build a small proof-of-concept model, define a set of plausible scenarios, and examine how different decisions lead to different pathways. The focus will be on structuring and comparing scenarios. The final output will demonstrate how a digital twin can be used to organise, test, and interpret alternative futures in a systematic and visual way.

Requirement to be on campus: No