Scientist, Laboratory of Metagenomic Technologies & Microbial Systems (GIS)

We are looking for a motivated research fellow who is keen on studying how microbes contribute to common skin diseases such as eczema and acne. Our lab is focused on leveraging new technologies to better study microbial populations and their impact on human health. We have extensive experience in metagenomic analysis in clinically or commercially important contexts such as uncovering distinct microbial communities in households affected by paediatric eczema (PMID: 35318044) and revealing the microbial basis of body odour in teenagers (PMID: 30497517). We are now focusing on applying various -omics capabilities to larger sized, longitudinal disease cohorts to understand how microbial pathways and biomarkers change with disease progression and treatment. The successful candidate will have the opportunity to work in a highly interdisciplinary environment, with unique access to next-generation sequencing technologies and a wide range of samples. Primary responsibilities include analyzing metagenomes, metatranscriptomes, leading associated publications and presenting findings at conferences. Project findings will have direct applications to ongoing and new microbiome research projects in the lab, particularly in skin diseases.

The Nagarajan Lab is based in the Genome Institute of Singapore (GIS) which is thenational flagship program for genomic sciences in Singapore with a mission to use genomics to improve public health and national prosperity. Singapore is a highly cosmopolitan city-state at the cross-roads of Asia with one of the highest standards of living in the world. The GIS provides an internationally competitive remunerationpackage and relocation assistance.

Requirements:

• PhD in Biology, Microbial Ecology, Computational Biology or a related field.
• Strong analytical and programming skills (Python, R, UNIX environment etc)
• Expertise in processing and analysing next generation sequencing (NGS) datasets
• Familiarity with statistical approaches in biological data analysis
• Strong interest in microbiome research