Honours and Masters project

Want to work with a real humanoid robot?

GINNY is a humanoid robotics project at Monash FIT exploring how robots can become more capable, adaptive, and socially intelligent partners.

You’ll work at the intersection of robot perception, LLMs, planning, and neuro-symbolic reasoning, building systems that allow a humanoid to understand its environment, reason about people and relationships, and make explainable decisions.

The key research question is simple but ambitious:

This project addresses the challenge of multi-view visual reasoning, where a model must integrate sparse observations from multiple viewpoints to build a coherent understanding of the underlying 3D world. Unlike standard single-image reasoning, multi-view reasoning requires the model to reason over view consistency, spatial layout, object relations, and hidden structure across observations.

This project focuses on developing data-driven benchmarks for robotic intelligence. The research will identify important limitations in current robotic systems, translate these limitations into measurable research questions, and design datasets and evaluation protocols that systematically test them.

Please note this advert is for a Summer Research Internship. It is not currently an advertisement for an honours or masters thesis project.

Please note you can ONLY apply for this internship via the  internship application form. 

Internship Project: https://www.monash.edu/study/fees-scholarships/scholarships/summer-winter

This project addresses a clear and urgent research problem situated at the intersection of technology adoption, academic labour, and equity.

Online learning environments provide students with access to a wide range of learning resources, including learning management systems (LMS), discussion forums, recorded lectures, assessments, and AI-supported learning tools. However, relatively little is known about how students navigate between these resources, how knowledge shared in one platform is transferred to another, and which patterns of engagement are associated with effective learning.

Project background

Computing graduates are expected to possess more than technical skills. Industry increasingly values capabilities such as communication, teamwork, leadership, conflict resolution, stakeholder engagement, and project management. However, these skills are often taught separately, making it difficult for students to understand how they connect in professional practice.

Generative AI chatbots are increasingly being adopted in higher education to support student learning and provide timely responses to questions. Within the Faculty of Information Technology at Monash University, AI-powered chatbots have been integrated into Ed Discussion (Ed Forum) for several units to assist teaching teams in managing online discussions and supporting student engagement.

Game-based learning is increasingly used in Computing education to encourage active participation, collaboration, problem-solving and application of technical and professional knowledge. Activities can range from low-cost classroom games such as Jeopardy-style quizzes, Bingo and team challenges to sophisticated digital simulations in which students make decisions and experience their consequences.

Teamwork is a critical graduate capability in computing education, yet students often receive little meaningful feedback about how they collaborate until a project has finished. This limits opportunities for teams to identify issues early, improve their collaboration, and develop teamwork skills throughout the semester.