CSIRO Postdoctoral Fellowship in Machine Learning for eDNA Analysis

CSIRO • Perth, WA

10d

About The Position

CSIRO is seeking a creative and motivated Postdoctoral Research Fellow to join an innovative, multidisciplinary team advancing the science of environmental DNA (eDNA) and its application in marine ecosystem monitoring. Working alongside the Minderoo Foundation, the team is pushing the boundaries of eDNA research through the development of new enrichment, quantification, and computational methods. This role provides an exciting opportunity to apply advanced machine learning and statistical modelling techniques to large-scale, high-dimensional eDNA datasets collected from Australian coastal and deep-sea waters, including those gathered during RV Investigator voyages. The successful candidate will lead the analysis and interpretation of complex biodiversity data, helping to develop cutting-edge, taxon-independent analytical frameworks and predictive models that reveal subtle ecological patterns and early signals of marine ecosystem change. The position involves close collaboration with a diverse group of scientists and technicians across the entire eDNA workflow, from field sampling to bioinformatics and software development. We are looking for applicants with a strong computational background and a passion for extracting meaningful ecological insights from challenging datasets, who are keen to co-develop practical analytical tools for marine biodiversity monitoring and environmental decision-making.

Requirements

• A doctorate in a relevant discipline area, such as ecology, computational biology, bioinformatics, data science, or a closely related field.
• Please note: To be eligible for this role you must have at least 1 year and no more than 4 years (full-time equivalent) of relevant postdoctoral research experience.
• Demonstrated experience analysing large, complex, and high-dimensional biological or biodiversity datasets, such as those derived from eDNA, metabarcoding, metagenomics, or related approaches.
• Strong expertise in statistical modelling and machine learning, including experience applying supervised and/or unsupervised learning methods to ecological or biological data.
• Strong programming skills in R, Python, or similar, with the ability to write reproducible, well-documented code.
• Familiarity with high-performance computing (HPC) environments for large-scale data analysis, including workload managers such as SLURM.

Nice To Haves

• Experience with neural networks or deep learning approaches is highly relevant to this role.

Responsibilities

• Conduct large-scale, taxon-independent analyses of existing and newly generated eDNA metabarcoding datasets to characterise patterns in community composition and relate these to measured oceanographic and environmental variables (e.g. pressure/depth, oxygen, salinity), through the application of supervised machine-learning approaches such as neural networks (deep learning).
• Apply unsupervised machine-learning approaches, including sequence representation learning and clustering, to marine eDNA data to identify patterns of sequence similarity and novelty relative to known taxonomic groups, and to quantify the extent of uncharacterised biodiversity in surveyed ecosystems (e.g. Australia’s deep-sea environments).
• Lead the analysis and publication of research outcomes in high quality publications.
• Carry out innovative, impactful research of strategic importance to CSIRO that will, where possible, lead to novel and important scientific outcomes.
• Recognise and utilise opportunities for innovation and the generation of new theoretical perspectives, and progress opportunities for the further development or creation of new lines of research
• Utilise design thinking methodology to plan and prepare research proposals, and apply non-academic impact methodology to research projects
• Carry out research investigations requiring originality, creativity and innovation
• Record, manage, and analyse data/information using relevant domain data science techniques.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree