When Dr Renuka Sharma, Data Scientist (Computer Vision and Ecology) at QCIF Digital Research was completing her PhD, she had the opportunity to collaborate on an industry-based research project.

The experience of working with real datasets and developing new systems gave her the confidence to translate academic research into real-world solutions. And now she wants to give back.

“During my joint PhD at Monash University and IIT Bombay, I collaborated with Woodside Energy. As part of that project, I developed a corrosion detection system using computer vision, while also designing generalised machine learning models for anomaly detection across diverse data domains.”

That experience shaped her motivation to “see the other side of this kind of collaboration” and supervise two groups of students from Bachelor of Information Technology and Master of Information Technology as part of Queensland University of Technology’s (QUT) Work Integrated Learning (WIL) program, allowing them to gain hands-on experience in data intensive environments.

Both projects run over a full year. Students recently completed the first phase (July–November 2025) and will continue their work from February to June 2026.

Applying machine learning and AI to conservation and healthcare

QUT’s WIL program is designed to give students practical experience before they graduate through internships, placements and real industry projects.

Supervised by Dr Sharma, the students are applying advanced data science, machine learning and AI techniques to two of QCIF’s existing research initiatives.

The first project focused on enhancing machine learning and computer vision models to detect and monitor Australian wildlife using visual data, including camera-trap images, as part of the Wildlife Observatory of Australia (WildObs) project. A key goal is to assess existing methods and customise them to better address the unique challenges of identifying Australian species, supporting conservation and strengthening our understanding of ecosystem health.

The second project focused on exploring how human behaviour can be recognised in healthcare environments without relying on vision-based data, which often captures identifiable information.

Developing machine learning models using alternative sensing modalities, such as wearable inertial measurement units (IMUs), radar and motion-based environmental sensors, this project aims to unravel the complexity of building accurate and reliable models from these nonvisual datasets, paving the way for safer and more privacy-preserving healthcare technologies.

Investing in future focused digital skills

Dr Shailesh Palekar, Industry Liaison Coordinator at QUT’s Faculty of Science, welcomes QCIF’s involvement and emphasises QCIF’s commitment to fostering the next generation of IT professionals.  

“QCIF’s dedication to nurturing emerging talent through hands‑on and insightful IT projects is what makes QUT's, Faculty of Science Work Integrated Learning program so impactful. When IT students are trusted to contribute to meaningful, real‑world research, they not only build technical skills—they build confidence, purpose, and a genuine connection to the IT industry they’re entering.”

QCIF Digital Research is proud to collaborate in initiatives that help students build both technical capability and professional confidence. WIL opportunities strongly align with QCIF’s mission to grow Australia’s digital research capacity and support the development of a skilled workforce for data intensive and research driven industries.

This mission is at the core of QCIF Skills & Training. In addition to delivering training, the team builds communities of practice, coordinates internship placement programs, and creates opportunities for researchers to connect, collaborate, and apply their skills in real-world environments. 

Opportunities at QCIF

QCIF’s Internship Program provides Masters students and penultimate or final year undergraduates the opportunity to work in data intensive environments and apply their technical skills in provisioning national data and digital research infrastructure.

As demand for digital and data expertise continues to grow, partnerships like the QUT–QCIF Work Integrated Learning collaboration demonstrate the value of industry and universities working together to create meaningful learning opportunities — delivering benefits for students, organisations and the broader research ecosystem.

QCIF looks forward to welcoming more students through future Work Integrated Learning and internship placements.

For more information and to contact us about future opportunities, contact training@qcif.edu.au.