Research Fellow – Data Science & Biological Chemistry Full-time

Research Fellow – Data Science & Biological Chemistry

This Research Fellow position is a strategically important and rare opportunity to strengthen interdisciplinary collaboration across the Faculty of Information Technology (Department of Data Science) and the Faculty of Science (School of Chemistry). The position is part of a project entitled “Conserving Endangered Australian Species Through Reproductive Hormone Analysis” an ARC Linkage Project, supervised by Professor Enes Makalic (Faculty of Information Technology) and Associate Professor Lisa Martin (Faculty of Science). The Research Fellow position is essential for delivering the project's cross-cutting computational and bioanalytical objectives and will involve developing a database to record and access clinical data from a wide variety of animal species and also to develop a Generative AI model which will predict the fertility stages of species based on analytical information from blood (or body fluid) samples.

The successful candidate will work closely with the project Chief Investigators to develop a secure, scalable database to capture and curate clinical (veterinary) and analytical data across multiple animal species, supporting both research integrity and long-term data accessibility. In parallel, the role will involve development of a generative AI model capable of predicting reproductive and fertility stages based on biochemical data derived from blood and other biological fluids. This predictive capability is central to the project's translational impact in wildlife conservation and reproductive management.

The Research Fellow position will also include development of tools for detection of steroid hormones at ultra-low detection limits using biophysical methods.

The role is intentionally structured as a 50:50 appointment between the Faculty of Information Technology (Data Science) and the Faculty of Science (Chemistry), reflecting the genuinely interdisciplinary nature of the research and ensuring alignment with both computational innovation and analytical laboratory development. This joint appointment is critical to achieving the project's objectives and to building sustained cross-faculty research capability in AI-enabled biosensing and conservation science.

The successful candidate will have some experience with Generative Artificial Intelligence (GenAI) models as well as steroid hormones or their biosynthetic pathways.

In addition, the position will contribute to the development of advanced biophysical detection tools capable of measuring steroid hormones at ultra-low concentrations, integrating bioanalytical chemistry with data-driven modelling approaches.