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

Explore adaptive AI agents capable of continual learning, dynamic knowledge integration, and trustworthy performance in evolving environments.

Best for:Best for AI/ML researchers with a strong foundation in deep learning, large language models, and Python, seeking to explore adaptive agentic systems and knowledge-grounded reasoning.

Monash University · Melbourne, Australia · R1 · First-stage researcher

More professors at Monash University

Self-evolving agentic AIContinual learningMulti-agent systemsKnowledge-grounded reasoningAdaptive AI systemsTrustworthy AI

Deadline

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Funding

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Start date

flexible (commence in the year of the offer)

Full-time

Location

Melbourne

Australia

Overview

This PhD position at Monash University, in collaboration with CSIRO and Maincode, focuses on developing self-evolving agentic AI systems. These systems aim to autonomously acquire new knowledge, reorganise collaboration structures, and improve performance over time. The project combines research in continual learning, multi-agent systems, and knowledge-grounded reasoning to create adaptive AI capable of sustained reliability in dynamic environments. The successful candidate will work with leading experts across academia and industry, gaining access to advanced computational resources and opportunities for real-world application testing. The role is ideal for candidates passionate about interdisciplinary AI research and eager to contribute to cutting-edge advancements in agentic systems.

agentic AIcontinual learningmulti-agent systemsknowledge graphsadaptive AILLMsPythonPyTorch

What you'll do

Develop self-evolving agentic AI systems capable of continual knowledge acquisition and dynamic reorganization of collaboration structures.
Investigate methods for continual knowledge integration and adaptive multi-agent orchestration using structured resources like ontologies and knowledge graphs.
Design and implement trustworthy, efficient, and scalable agent systems for complex tasks.
Collaborate with interdisciplinary teams across Monash University, CSIRO, and Maincode to transition research into real-world applications.
Conduct cutting-edge research in AI/ML/LLM topics, leveraging advanced computational resources.
Publish findings in top-tier academic venues and contribute to the advancement of adaptive AI systems.
Engage in interdisciplinary research and effectively communicate research ideas and progress to diverse stakeholders.

Requirements

Degree required: A bachelor's degree of at least four years in computer science, artificial intelligence, machine learning, or a closely related field, including a research thesis or project with a minimum overall average grade equivalent to First Class Honours; or a master's degree in a relevant discipline with a research thesis/project equivalent to at least 25% of one year of full-time study, with a minimum overall average grade equivalent to First Class Honours.

Degree offered: PhD

Language: English; proficiency meeting Monash University's English language requirements

Instruction: English

Required skills

Strong background in computer science, artificial intelligence, or machine learning
Solid understanding of deep learning and large language models
Proficiency in Python and experience with machine learning frameworks such as PyTorch or TensorFlow
Experience with research projects, publications, or advanced coursework in AI or data science
Strong analytical and problem-solving abilities
Good programming skills and strong written and verbal communication skills

Nice to have

Familiarity with natural language processing, multi-agent systems, reinforcement learning, or knowledge representationExperience with research projects, publications, or advanced coursework in AI or data scienceEnthusiasm for interdisciplinary research and collaboration within a research team

Eligibility

Australian citizen, Permanent Resident, or New Zealand citizen
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 with approval)
Pass mandatory government background checks for CSIRO onboarding (4-8 weeks)

Funding & terms

Type

Unknown

Amount

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Duration

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Programme

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How to apply

Deadline: 2026-11-13 (Australia/Sydney)

Submit an Expression of Interest (EOI) to fit-graduate.research@monash.edu. Include a CV, academic transcripts with grading scale, a cover letter, and a draft research proposal of up to 2 pages addressing the project's research objectives. Candidates who pass the initial review will be invited for further discussions with the supervisory team before submitting a full application.

Documents required