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PhD in Artificial Intelligence and Biodiversity Conservation Language Barriers University of Queensland in Australia
Degree Level
PhD
Field of study
Computer Science
Funding
Competitive scholarships are available to eligible domestic and international applicants through the University of Queensland Graduate Research School Scholarships. Funding is competitive and assessed on academic performance, evidence of research capability, project quality, the research environment, and the proposed advisory team.
Deadline
Oct 19, 2026
Country
Australia
University
University of Queensland

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About this position
PhD opportunity at the University of Queensland in artificial intelligence, biodiversity conservation, language barriers, evidence synthesis, and multilingual scientific communication.
The project asks whether AI has reduced language barriers in conservation science across three pathways: generating evidence, synthesising evidence, and applying evidence in practice. The research is based in the School of the Environment and linked to the translatE project and the broader Kaizen Conservation Group.
Possible methods include global surveys, interviews or focus groups, bibliometric analyses, and analyses of systematic reviews or biodiversity assessments. The project is interdisciplinary and can be shaped with the supervisory team, with room to adapt questions and methods to the successful candidate’s strengths.
Eligibility and profile: applicants should meet UQ’s PhD admission and English-language requirements. Helpful backgrounds include ecology, conservation science, evidence synthesis, information science, or related interdisciplinary fields. Experience with Python, R, survey/interview research, systematic reviews/meta-analysis, bibliometrics, text analysis, or working across languages/cultural contexts would be valuable. Proficiency in a language other than English is welcome but not essential.
Funding: competitive scholarships are available for eligible domestic and international applicants through the UQ Graduate Research School Scholarships. Selection is competitive and considers academic performance, research capability, project quality, the research environment, and the advisory team.
Location: Brisbane, Australia.
Application process: first email Associate Professor Tatsuya Amano with a CV, transcripts, a brief research summary, and a recent writing sample. Shortlisted candidates will be invited to discuss research questions and scholarship competitiveness before a formal expression of interest is submitted to UQ. International applicants seeking to commence in Research Quarter 4 2027 or early 2028 have a closing date of 2026-10-19.
Funding details
Competitive scholarships are available to eligible domestic and international applicants through the University of Queensland Graduate Research School Scholarships. Funding is competitive and assessed on academic performance, evidence of research capability, project quality, the research environment, and the proposed advisory team.
What's required
Applicants must meet the University of Queensland requirements for admission to the Doctor of Philosophy program, including academic and English-language requirements. A background in ecology or conservation science, evidence synthesis, information science, or a related interdisciplinary field is relevant. Valuable experience includes quantitative data analysis (Python and R), survey/interview/focus-group research, systematic reviews or meta-analysis, bibliometric or text-based analysis, and working across languages or cultural contexts. Proficiency in a language other than English is highly relevant but not essential.
How to apply
First contact Associate Professor Tatsuya Amano by email with your CV, transcripts, a brief description of your research experience and skills, and a recent writing sample. Do not submit a formal expression of interest unless invited to interview. Shortlisted candidates will then be asked to discuss research questions and scholarship competitiveness before applying formally to UQ.
More information can be found here
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