About This Opportunity
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
* 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, including studies on User‐LLM Collaboration in relevance judgement.
* Experimental Protocols and Evaluation Methodologies: 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.
About You
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:
o Large Language Models (LLMs)
o Information Retrieval (IR)
o Natural Language Processing (NLP)
o Retrieval‐Augmented Generation (RAG)
o Human‐AI collaboration systems
o Interactive information retrieval
o Systematic review automation
o 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.
About UQ
UQ offers world‐class facilities and equipment, grant writing support, flexible working arrangements, and opportunities for career progression through the academic promotions process. The institution values interdisciplinary collaboration, high‐quality research, and professional support for staff development.
Interested?
Applications close Sunday June 14th 2026 at 11:00pm AEST. For application inquiries, contact the Talent Acquisition team at talent@uq.edu.au, stating the job reference number (R-65547) in the subject line.
Other Information
* Pre‐employment checks may include verification of right to work in Australia, qualifications and criminal history checks, as well as checks relating to gender‐based violence matters or other integrity and conduct requirements.
* You must maintain unrestricted work rights in Australia for the duration of this appointment to apply. Employer‐sponsored work rights are not available for this appointment.
* We are dedicated to equity, diversity, and inclusion. We recognise that career pathways and opportunities differ, and encourage applications from candidates who may not meet every criterion but can demonstrate their potential relative to opportunity. We are also happy to support any accessibility needs throughout the recruitment process. Please let us know how we can help.
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