Job Description:
Job Title: Bioinformatics Scientist / Computational Biologist
Location: Melbourne, Victoria, Australia
Job Description:
We are seeking a highly motivated Bioinformatics Scientist / Computational Biologist to join our research-driven team based in Melbourne, a leading hub for biomedical innovation in Australia. This role is ideal for individuals passionate about using computational tools to unravel complex biological questions and support life sciences research.
The successful candidate will work on high-impact projects involving genomics, transcriptomics, and multi-omics data analysis, contributing to advancements in precision medicine, drug discovery, and molecular diagnostics. You will collaborate closely with molecular biologists, clinicians, and data scientists to process large-scale datasets, develop analytical pipelines, and visualize biological insights.
Key Responsibilities:
Analyze next-generation sequencing (NGS) data (e.g., RNA-seq, WGS, scRNA-seq)
Design and implement bioinformatics workflows using Python, R, and Linux-based systems
Interpret data and contribute to publications, grant applications, and presentations
Maintain and document reproducible research pipelines
Stay current with advances in computational biology tools and methodologies
Qualifications:
MSc or PhD in Bioinformatics, Computational Biology, or related field
Proven experience with biological data analysis
Strong problem-solving and communication skills
Job Requirement:
Job Title: Bioinformatics Scientist / Computational Biologist
Location: Sydney, New South Wales, Australia
Job Requirements:
We are looking for an experienced and detail-oriented Bioinformatics Scientist / Computational Biologist to join our multidisciplinary research team in Sydney, Australia’s leading city for biomedical research and innovation.
Essential Requirements:
A PhD or Master’s degree in Bioinformatics, Computational Biology, Genomics, or a related field.
Minimum 2–3 years of hands-on experience in analyzing high-throughput sequencing data (e.g., RNA-seq, WGS, exome, or single-cell datasets).
Proficiency in scripting and programming languages such as Python, R, and Bash, with experience in Linux/Unixenvironments.
Familiarity with bioinformatics tools and pipelines such as STAR, GATK, BWA, DESeq2, edgeR, and workflow managers like Snakemake or Nextflow.
Strong understanding of molecular biology, genomics, and statistical analysis methods.
Experience with cloud computing platforms (e.g., AWS, GCP) and version control (e.g., Git).
Desirable:
Experience working in a clinical or diagnostic setting.
Familiarity with databases such as Ensembl, NCBI, and UCSC Genome Browser.
