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

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

This is an unprecedented opportunity for an outstanding data science and AI PhD candidate interested in agentic AI, supervised by Dr Teresa Wang and Dr Tongtong Wu at Monash University, jointly with Dr He Zhao and Dr Dan Steinberg from CSIRO, Dr Yue Yang and David Lemphers from Maincode.

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 student will be able work on cutting-edge AI/ML/LLM topics closely with leading experts in Monash University, CSIRO, and Maincode and access to the computational resources in these organisations. The student will also gain valuable industrial experience from Maincode who could provide a solid testbed for the work, which would help transition the research into real-world applications.

To be considered for this opportunity you should fulfil the eligibility requirements listed below.

• Be an Australian citizen or Permanent Resident, or a New Zealand citizen.
• A bachelor's degree of at least four years in a relevant discipline, which includes a research thesis or project, with a minimum overall average grade of an honours degree equivalent to the First Class Honours; or
• A master's degree in a relevant discipline which includes a research thesis or project equivalent to at least 25 percent of one year of full-time study, with a minimum overall average grade of honours equivalent to the First Class Honours; or
• A qualification, or combination of qualifications and relevant professional experience, deemed equivalent by the GRC (or delegate).
• Meet university English language requirements.
• Not have previously completed a PhD.
• Be able to commence the Program in the year of the offer.
• Enrol as a full-time PhD student. Part-time arrangements may be considered if approved by the supervisory team and in accordance with university policy.
• Be prepared to be located at the project location(s) that Monash University has approved and, if required, comply with Monash's external enrolment procedures.
• Be prepared to undergo onboarding to CSIRO, which will include passing mandatory government background checks (allow for between 4 to 8 weeks) and complete any other CSIRO requirements.

The ideal PhD candidate will have:

• 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

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.

This position has a two-stage selection process:

Stage 1: Please submit to fit-graduate.research@monash.edu.

With the EOI please include the documents – CV, academic transcripts and grading scale, a cover letter and a draft research proposal of up to 2 pages, responding to one or more of the above research objectives.

Stage 2: Candidates who pass this stage of the selection process will be invited to discuss their ideas with the supervisory team before developing and submitting a full application.

Applications Close: Friday 13th November 2026, 11:55 pm AEST