Research Assistant (Environmental Microbiology)

About the role

NTU is seeking a motivated Research Assistant to support research on quantitative microbial analysis using multi-omics approaches. The successful candidate contributes to laboratory and computational workflows, including microbial cultivation, multi-omics data generation and analysis, and modelling of microbial systems, within a multidisciplinary research environment covering microbial ecology, systems biology, biotechnology and related fields.

Key responsibilities

• Conduct quantitative microbial research using genomic, transcriptomic, metabolomic, fluxomic and other multi-omics approaches.
• Perform laboratory-based molecular microbiology experiments, including microbial cultivation and sample preparation.
• Analyse and interpret complex biological datasets using computational and bioinformatics tools.
• Develop and apply metabolic, ecological, statistical or related computational models.
• Maintain and optimise microbial culture systems, including bioreactors and defined culture platforms where applicable.
• Prepare research reports, technical documentation, manuscripts, presentations and other project materials.
• Collaborate with researchers, technical staff and external stakeholders, and maintain accurate experimental records and lab safety compliance.
• Contribute to grant applications, project reporting and dissemination of findings where required.

Required profile

• Bachelor's degree in Microbiology, Molecular Biology, Biotechnology, Bioengineering, Bioinformatics, Life Sciences or a related discipline; a PhD is advantageous.
• Demonstrated experience in molecular microbiology and multi-omics research methods.
• Experience analysing genomic, transcriptomic, metabolomic or fluxomic datasets, and knowledge of bioinformatics workflows for amplicon sequencing, metagenomics or similar platforms.
• Experience with microbial culture systems; exposure to bioreactors or defined culture systems is an advantage.
• Familiarity with metabolic, ecological or statistical modelling approaches.
• Strong scientific writing, data interpretation and communication skills, and ability to work independently and in multidisciplinary teams.

Only shortlisted candidates will be notified.