Publisher
source

University of Newcastle

National Industry PhD Program

PhD in Hierarchical Imitation Learning for Long-Horizon Robotic Tasks The University of Newcastle in Australia

Degree Level

PhD

Field of study

Computer Science

Funding

Funded PhD through the Australian Government’s National Industry PhD Program. Includes a National Industry PhD Award Scholarship (Industry Linked) of $38,938 per annum at the 2026 rate, indexed annually, for 3.5 years, with tuition fee scholarship, up to $1,500 relocation allowance if applicable, an additional $7,135 government top-up, and a $10,000 per annum top-up from Imitation Machines Pty Ltd for 3.5 years.

Deadline

Nov 6, 2026

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Country

Australia

University

University of Newcastle

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Keywords

Computer Science
Electrical Engineering
Information Technology
Deep Learning
Mathematics
Python Programming
Reinforcement Learning
Robotics
Imitation Learning
Control System
ML

Suggested scholarships

About this position

PhD opportunity at The University of Newcastle, Australia, in Hierarchical Imitation Learning for Long-Horizon Robotic Tasks.

This project develops a novel framework combining hierarchical imitation learning and reinforcement learning to help robots complete long-horizon, multi-step tasks such as production line assembly and household chores. The research focuses on learning high-level subgoals from demonstrations and low-level control policies for execution, with validation through continuous real-world deployment on an industry partner’s robot learning platform.

The position is funded through the Australian Government’s National Industry PhD Program in partnership with Imitation Machines Pty Ltd, an Australian AI/robotics startup. Funding includes a scholarship of $38,938 per annum (2026 rate), indexed annually, for 3.5 years, plus tuition fee scholarship, up to $1,500 relocation allowance if applicable, a $7,135 government top-up, and a $10,000 per annum company top-up.

Eligibility highlights: applicants should have a Master’s degree or equivalent in Computer Science, Robotics, AI, Engineering, or a related quantitative discipline; experience in machine learning, deep learning, or reinforcement learning; strong Python skills and familiarity with PyTorch or TensorFlow; experience with robotic systems, control, or simulation; strong mathematical foundations; and a publication track record. Domestic students only. Desirable experience includes imitation learning, hierarchical learning, or sim-to-real transfer.

To apply, email your expression of interest to [email protected] with transcripts, CV, a brief research statement, and a proposal linked to the project. Deadline: 06 November 2026 (5pm).

Funding details

Funded PhD through the Australian Government’s National Industry PhD Program. Includes a National Industry PhD Award Scholarship (Industry Linked) of $38,938 per annum at the 2026 rate, indexed annually, for 3.5 years, with tuition fee scholarship, up to $1,500 relocation allowance if applicable, an additional $7,135 government top-up, and a $10,000 per annum top-up from Imitation Machines Pty Ltd for 3.5 years.

What's required

Applicants must meet the minimum admission criteria and hold a Master’s degree or equivalent in Computer Science, Robotics, AI, Engineering, or a related quantitative discipline. Required experience includes machine learning knowledge (deep learning, reinforcement learning, or related areas), Python programming, familiarity with deep learning frameworks such as PyTorch or TensorFlow, experience with robotic systems, control or simulation environments, strong mathematical foundations, publication track record in relevant venues, and excellent interpersonal skills for collaborative industry work. Familiarity with imitation learning, hierarchical learning, or sim-to-real transfer is desirable. Domestic students only.

How to apply

Email your expression of interest to [email protected]. Include scanned academic transcripts, CV, a brief statement of research interests, and a proposal linking your background to the project. Apply by 5pm on 06 November 2026.

More information can be found here

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