Environmental Genomics Project:• Processed and analysed ~9 TB of genomic eDNA data from ~30,000 samples using Gadi supercomputer and GHAP pipeline.• Conducted data preprocessing using Python (pandas) to average replicates, remove outliers, and generate tables at different taxonomies• Consolidated and grouped environmental data by various time periods (e.g., 5, 30, 45 days).• Applied multivariate analyses (PCA, PERMANOVA, ANOSIM, mvabund, CCA) in Python and R to study biological and environmental interactions.• Developed machine learning models (SVM, KNN, Naive Bayes, Logistic regression, Neural Networks, Random Forest) to predict ecological responses.• Visualisation of results was conducted using Python (matplotlib and seaborn).• Random Forest model identified key features for biotic and environmental data, aiding in bioremediation efforts.

Researching subsurface microbes:• Processing genomic data using the GHAP pipeline which is an in-house clustering and classification pipeline built around tools from Usearch and RDP, combined with locally-written tools for demultiplexing and generating operational taxonomic unit (OTU) tables.• Metagenomic and microbial analysis by performing community composition analysis of microbial samples to identify taxonomic diversity and ecological interactions.• Researching, drafting, and writing scientific articles for publication.