CSIRO Industry PhD Scholarship: Self-Evolving Agents: Continual, Trustworthy, and Resource-Efficient Agentic AI

Funding and duration

four year scholarship package totalling approximately $48,000 per annum tax exempt (2026 rate); four-year scholarship and research training program.

Project overview

Project title

Self-Evolving Agents: Continual, Trustworthy, and Resource-Efficient Agentic AI

Project description

Advances in large language models and agentic AI have enabled autonomous systems that can reason, plan, retrieve information, and use external tools. However, most existing agent frameworks remain largely static: their knowledge, internal organisation, and coordination strategies are typically predefined. This makes them brittle in dynamic environments where information, tasks, and requirements continuously evolve.

This project investigates self-evolving agentic AI systems, i.e., adaptive AI agents that can continually acquire new knowledge, reorganise their collaboration structures, and improve their performance over time. The research will explore methods for continual knowledge integration, adaptive multi-agent orchestration, and knowledge-grounded reasoning using structured resources such as ontologies and knowledge graphs. It will also study approaches for building efficient and trustworthy agent systems that can scale to complex tasks.

The project aims to develop a unified framework for building agentic AI systems that can operate as long-term collaborators, continuously learning and adapting as their environments and objectives change. By combining ideas from continual learning, multi-agent systems, and knowledge-grounded reasoning, this research seeks to advance the foundations of adaptive AI systems capable of sustained, reliable performance in evolving knowledge environments.

Supervisory team

University

Name of university supervisor | Weiqing WangTongtong Wu

Name of university | Monash University

Email address | teresa.wang@monash.edutongtong.wu@monash.edu

Faculty | Faculty of Information Technology

CSIRO

Name of CSIRO supervisor | He ZhaoDan Steinberg

Email address | he.zhao@csiro.audan.steinberg@csiro.au

CSIRO Research Unit | Technology

Industry

Name of industry supervisor | Yue YangDavid Lemphers

Name of organisation | Maincode Pty Ltd

Email address | yue@maincode.comdave@maincode.com

Further details

Primary location of student | Monash University, Wellington Road, Clayton VIC 3800, Australia

Industry engagement component location | Maincode Pty Ltd, Level 44, 360 Elizabeth Street, Melbourne VIC 3000, Australia

Other locations | CSIRO Clayton, Research Way, Clayton VIC 3168, Australia

Ideal student skillset | A strong background in computer science, artificial intelligence, machine learning, or a closely related field. A solid understanding of machine learning concepts, particularly deep learning and large language models, is desirable. Experience with Python and common machine learning frameworks (such as PyTorch or TensorFlow) is highly valued. Familiarity with topics such as natural language processing, multi-agent systems, reinforcement learning, or knowledge representation would be advantageous but is not strictly required.Strong analytical and problem-solving abilities, as well as an interest in developing and evaluating new AI methods. Experience with research projects, publications, or advanced coursework in AI or data science will be considered favourably. Good programming skills and strong written and verbal communication skills. Enthusiasm for interdisciplinary research and the ability to collaborate effectively within a research team are also important.

Apply | Open until position filled

Apply | Monash