MONASH UNIVERSITY
2 days ago
PhD: Self-Evolving Agents: Continual, Trustworthy, and Resource-Efficient Agentic AI Monash University in Australia
Degree Level
PhD
Field of study
Computer Science
Funding
Four-year scholarship package totalling approximately AUD 48,000 per annum (2026 rate with annual indexation), plus a four-year Project Expense and Development package starting from AUD 13,000 per annum, FIT Candidature Funding of AUD 4,000 for the candidature, and up to AUD 1,265 one-off travel grant.
Deadline
Nov 13, 2026
Country
Australia
University
MONASH UNIVERSITY
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About this position
This PhD project sits within the CSIRO Industry PhD scholarship program and is hosted at Monash University in collaboration with CSIRO and Maincode. The research focuses on self-evolving agentic AI: autonomous AI agents that can continue learning, reorganise how they collaborate, and improve as their environments, tasks, and information change over time.
The project addresses a major limitation of many current agent frameworks: they are largely static, with predefined knowledge and coordination strategies that can become brittle in dynamic settings. Here, the student will investigate methods for continual knowledge integration, adaptive multi-agent orchestration, and knowledge-grounded reasoning using structured resources such as ontologies and knowledge graphs.
The research also aims to develop approaches for building efficient and trustworthy agent systems that can scale to complex tasks and operate as long-term collaborators. The successful candidate will work closely with academic and industry experts across Monash University, CSIRO, and Maincode, with access to computational resources in these organisations and an industrial testbed to help translate the research toward real-world use.
Supervision is provided by Dr Teresa Wang and Dr Tongtong Wu at Monash University, together with Dr He Zhao and Dr Dan Steinberg from CSIRO, and Dr Yue Yang and David Lemphers from Maincode.
Funding details
Four-year scholarship package totalling approximately AUD 48,000 per annum (2026 rate with annual indexation), plus a four-year Project Expense and Development package starting from AUD 13,000 per annum, FIT Candidature Funding of AUD 4,000 for the candidature, and up to AUD 1,265 one-off travel grant.
What's required
Applicants must be Australian citizens, Permanent Residents, or New Zealand citizens. They should hold a relevant bachelor’s degree of at least four years with a research thesis or project and an honours-equivalent First Class Honours average, or a relevant master’s degree with a substantial research thesis/project and honours-equivalent First Class Honours average, or an equivalent qualification/professional experience accepted by the GRC. Applicants must meet Monash University English language requirements, must not have previously completed a PhD, must be able to commence in the year of the offer, and must enrol as a full-time PhD student (part-time only if approved). Candidates should be prepared to be located at the approved project location and complete any required Monash external enrolment procedures and CSIRO onboarding, including mandatory government background checks. The ideal candidate will have a strong background in computer science, artificial intelligence, machine learning, or a closely related field; a solid understanding of deep learning and large language models; experience with Python and frameworks such as PyTorch or TensorFlow; and familiarity with natural language processing, multi-agent systems, reinforcement learning, or knowledge representation is advantageous. Strong analytical and problem-solving skills, good programming ability, strong written and verbal communication skills, experience with research projects/publications or advanced AI/data science coursework, and an ability to collaborate in an interdisciplinary team are preferred.
How to apply
Submit an EOI by email to [email protected] with a CV, academic transcripts and grading scale, a cover letter, and a draft research proposal of up to 2 pages. If shortlisted, you will be invited to discuss your ideas with the supervisory team before submitting a full application.
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Make your next move.
Prepare your application
Review drafts and confirm requirements with the provider.