Job Description

The Faculty of Dentistry at the National University of Singapore (NUS) invites applications for a Senior Bioinformatician to support and lead advanced research with clinical and translational impact, driving the generation of high-quality data and analyses that underpin high-impact publications and grant applications.

Key Responsibilities

• Provide intellectual and technical leadership in bioinformatics and computational biology to support high-impact, hypothesis-driven research.
• Lead and support multi-omics data analysis, including microbiome (16S, metagenomics, metatranscriptomics), bulk and single-cell/spatial transcriptomics, proteomics, metabolomics, and integration with clinical datasets.
• Design, implement, and maintain robust, reproducible bioinformatics pipelines across Illumina, Nanopore, and 10x Genomics platforms.
• Perform advanced downstream analyses including functional enrichment (GO, KEGG), network analysis, genome assembly and binning, systems biology, and multi-omics integration.
• Apply statistical modelling, machine learning, and deep learning approaches for biomarker discovery, disease stratification, prognostic modelling, and causal inference.
• Work closely with clinician-scientists to translate computational findings into clinically relevant insights and high-impact manuscripts.
• Contribute substantially to grant development, particularly NMRC and other national or international funding schemes, through study design, analytics planning, and generation of compelling preliminary data.
• Mentor postgraduate students and research staff in bioinformatics and data science.
• Publish research findings in high-impact peer-reviewed journals.

Qualifications

• PhD in Bioinformatics, Computational Biology, Biostatistics, Computer Science, or a closely related discipline, with substantial postdoctoral or equivalent research experience.
• Demonstrated expertise in microbiome and complex community analysis, including metagenomic assembly, binning, and functional profiling.
• Strong experience in multi-omics data integration spanning genomics, transcriptomics (bulk, single-cell, spatial), proteomics, and metabolomics.
• Solid background in statistical analysis and survival modelling for biomedical and clinical research.
• Proven experience applying machine learning and/or deep learning methods to biological or clinical datasets.
• Proficiency in Python and R, with strong experience in Linux/HPC environments and workflow automation.
• Track record of publications in high-impact journals and contributions to competitive grant applications.
• Good scientific writing, communication, and collaborative skills.

Desirable Attributes

• Experience supporting competitive grant applications.
• Ability to independently conceptualize analytics-driven research questions that lead to high quality publications.
• Experience working in interdisciplinary teams spanning clinical, wet-lab, and computational research.
• Familiarity with cloud computing, database management, and scalable analysis infrastructure.
• Strong mentoring orientation and ability to raise the overall research quality of collaborative teams.