Postdoctoral Research Fellow – Agentic AI for Systematic Reviews and Human–LLM Collaboration Full-time

Postdoctoral Research Fellow

We are seeking an outstanding Postdoctoral Research Fellow to contribute to ambitious, high-impact research at the intersection of Artificial Intelligence, Information Retrieval, Natural Language Processing, digital health, and Human-Computer Interaction. Working within a collaborative and multidisciplinary research environment, you will help design and deliver novel methods and open-source systems that leverage Large Language Models to support evidence synthesis, relevance assessment, and human–AI collaborative decision-making.

This role offers a rare opportunity to combine methodological innovation with practical research software development, contributing to globally relevant research outcomes and platforms.

Key responsibilities will include:

• Research and Algorithm Development: Conduct high-quality research in areas related to LLM-assisted systematic reviews, IR, NLP, Retrieval-Augmented Generation (RAG), and human–AI collaboration. Develop novel approaches for literature screening, relevance assessment, evidence synthesis, AI-assisted ranking, and collaborative review workflows. Lead and contribute to publications in leading venues and journals in Information Retrieval, AI, NLP, digital health, and health informatics. Assist with the preparation of grant applications and collaborative research proposals.
• Research Software and Platform Development: Design, develop, and maintain scalable research software systems supporting AI-assisted systematic review workflows and relevance assessment tasks. Contribute to the development of open-source platforms integrating LLMs into evidence synthesis workflows. Develop and maintain APIs, retrieval pipelines, vector search systems, databases, and cloud-based infrastructure for LLM-assisted applications. Support experimentation with commercial and open-source LLMs, semantic retrieval systems, and RAG pipelines.
• User Studies and Human–AI Collaboration Research: Design and conduct user studies investigating the effectiveness, usability, trustworthiness, and cognitive impact of AI-assisted relevance assessment and systematic review systems. This includes research involving platforms for systematic reviews and projects on User–LLM Collaboration in relevance judgement.
• Develop experimental protocols and evaluation methodologies to study how different forms of LLM assistance influence human decision-making, screening behaviour, relevance judgements, efficiency, and perceived utility. Analyse user interaction data and contribute to publications related to human–AI collaboration, trustworthy AI, and interactive information retrieval.
• Supervision and Researcher Development: Provide supervision and mentoring to HDR students and research assistants, and contribute to the supervision of capstone and project-based students as required. Support a positive and inclusive research culture through collaborative project work, feedback, and shared scholarly practice.
• Citizenship and Service: Contribute to technical documentation, reproducible research workflows, software demonstrations, tutorials, workshops, and broader collaborative research activities. Actively foster a collegial, inclusive, and respectful research environment aligned with UQ values.

This is a research focused position. Further information can be found by viewing UQ's Criteria for Academic Performance.

Our ideal candidate will be a self-motivated, inquisitive, and solutions-focused researcher who thrives in interdisciplinary environments and is motivated to contribute meaningfully to collaborative research programs. You will combine strong technical capability with excellent communication skills and a commitment to high-quality, reproducible research.

You will have:

• Completion or near completion of a PhD in Computer Science, Artificial Intelligence, Information Retrieval, Natural Language Processing, Data Science, Human-Computer Interaction, Health Informatics, or a closely related field.
• Strong research track record relative to opportunity, demonstrated through publications in relevant venues.
• Demonstrated expertise in one or more of the following areas:
• Large Language Models (LLMs)
• Information Retrieval (IR)
• Natural Language Processing (NLP)
• Retrieval-Augmented Generation (RAG)
• Human–AI collaboration systems
• Interactive information retrieval
• Systematic review automation
• Machine learning and deep learning
• Experience conducting empirical evaluations, user studies, or experimental research involving human participants is highly desirable.
• Strong programming and software engineering skills, particularly in Python and modern AI/ML frameworks such as PyTorch, Hugging Face Transformers, LangChain, or LlamaIndex.
• Experience with backend or full-stack software development, APIs, databases, vector search systems, Docker, Kubernetes, and cloud platforms, such as AWS or GCP is desirable.
• Demonstrated ability to work collaboratively in interdisciplinary research environments and contribute to team-based research projects.
• Excellent written and verbal communication skills.

As part of the UQ community, you will have the opportunity to work alongside the brightest minds, who have joined us from all over the world, and within an environment where interdisciplinary collaborations are encouraged. As part of our commitment to excellence in research and professional practice in academic contexts, we are proud to provide our staff with access to world-class facilities and equipment, grant writing support, greater research funding opportunities, and other forms of staff support and development.

The greater benefits of joining the UQ community are broad: from being part of a Group of Eight university, to recognition of prior service with other Australian universities, up to 26 weeks of paid parental leave, 17.5% annual leave loading, flexible working arrangements, access to exclusive internal-only vacancies, and genuine career progression opportunities via the academic promotions process.

For more information about this opportunity, please contact Dr. Teerapong Leelanupab t.leelanupab@uq.edu.au. For application inquiries, please reach out to the Talent Acquisition team at talent@uq.edu.au, stating the job reference number (below) in the subject line.

When you apply, please ensure you upload a resume, cover letter, and responses to the 'About You' section. Please note that applications received via email will not be accepted.

Applications close Sunday June 14th 2026 at 11.00pm AEST (R-65547). Please note that interviews have been tentatively scheduled for Friday 19 June 2026.