Research Fellow – Data Science & Biological Chemistry
Job No.: 691543
Location: Clayton campus
Employment Type: Full-time
Duration: 12-month fixed-term appointment
Remuneration: $83,280 - $113,025 pa Level A (plus 17% employer superannuation)
- Amplify your impact at a world top 50 University
- Join our inclusive, collaborative community
- Be surrounded by extraordinary ideas - and the people who discover them
The Opportunity
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 data base 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.
About Monash University
At Monash, work feels different. There’s a sense of belonging, from contributing to something ground breaking – a place where great things happen.
We value difference and diversity, and welcome and celebrate everyone's contributions, lived experience and expertise. That’s why we champion an inclusive and respectful workplace culture where everyone is supported to succeed.
Some 20,000 staff work for Monash around the world. We have 95,000 students, four Australian campuses, and campuses in Malaysia and Indonesia. We also have a major presence in India and China, and a significant centre and research foundation in Italy.
In our short history, we have skyrocketed through global university rankings and established ourselves consistently among the world's best tertiary institutions. We rank in the world’s top-50 universities in rankings including the QS World University Rankings 2026.
Together with our commitment to academic freedom, you will have access to quality research facilities, infrastructure, world-class teaching spaces, and international collaboration opportunities.
Learn more about Monash.
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Monash supports flexible and hybrid working arrangements. We have a range of policies in place enabling staff to combine work and personal commitments. This includes supporting parents.
To Apply
For instructions on how to apply, please refer to
'How to apply for Monash Jobs'. Your application must address the Key Selection Criteria.
Diversity is one of our greatest strengths at Monash. We encourage applications from Aboriginal and Torres Strait Islander people, culturally and linguistically diverse people, people with disabilities, neurodivergent people, and people of all genders, sexualities, and age groups.
We are committed to fostering an inclusive and accessible recruitment process at Monash. If you need any reasonable adjustments, please contact us at hr-recruitment@monash.edu in an email titled 'Reasonable Adjustments Request' for a confidential discussion.
Your employment is contingent upon the satisfactory completion of all pre-employment and/or background checks required for the role, as determined by the University.
Enquiries:
Associate Professor Lisa Martin, School of Chemistry, Faculty of Science, lisa.martin@monash.edu
Professor Enes Makalic, Department of Data Science and Artificial Intelligence, Faculty of Information Technology, enes.makalic@monash.edu
Position Description: Research Fellow
Applications Close: Tuesday 7 April 2026, 11:55pm AEST
Supporting a diverse workforce
