Research Fellow, Bioinformatics / Molecular Epidemiology

Research Fellow – Bioinformatics / Molecular Epidemiology – Perinatal and Child Health and Development

A two-year position at the Singapore Institute for Clinical Sciences is available to a highly-motivated postdoctoral researcher in bioinformatics and/or molecular epidemiology in the area of reproductive, perinatal, and pediatric epidemiology, with particular training and expertise in transcriptomics and epigenomics. The candidate will develop a competitive research portfolio using class-leading multimodal data from up to three longitudinal birth cohort studies (GUSTO, N = ~1200 enrolled; S-PRESTO, N = ~1000 enrolled; and MAMS, N = ~2000 anticipated) in collaboration with local research hospitals, top research universities, government research entities, and a broad network of international collaborators.

The cohorts represent world-class efforts in deep pheno- and geno-typing, with available measures including: clinical characteristics; sociodemographics and spatiotemporal data; parenting and child behaviors; child neurodevelopmental assessment; state-of-the-art brain and body imaging; and numerous –omics (metabolome; (epi)genome; transcriptome; microbiome). A large biorepository (e.g. hair, saliva, urine, blood) has been formed. Details on available data and samples for GUSTO/S-PRESTO can be found at: gustodatavault.sg

Opportunities for further training, mentorship, and collaboration, including out-placement with a data science industry partner may be possible. Incumbent will have access to a highly interdisciplinary local research environment at SICS; A*STAR; Duke-NUS; NUS School of Public Health; NUS School of Medicine; NTU, etc. with the goal of supporting the incumbent’s long-term career goals. SICS is a dynamic working environment with dozens of postdoctoral peers working in such diverse domains as eating behaviors, neurodevelopment, allergy, growth and metabolism, parenting, -omics, brain and body imaging, and oral health.

The Fellow will work closely with the PI to develop research based on two competitively-funded projects:

1.     Placental multi-omics linking in vitro fertilization (IVF) and longitudinal child cardiometabolic outcomes. The Fellow will develop and apply methods to investigate mediating mechanisms between IVF and child outcomes utilizing placental multi-omic data (genomic, DNA methylation, and RNAseq).

·         Early work on this has been published in Huang, et al. Nature Communications. 12: 5613. 2021. The Fellow may contribute to further developing outlined approaches including methods for negative controls for unmeasured confounding, eQTLs, causal mediation, etc.

·         This work will contribute to a new consortium on placental transcriptomics that includes one of the largest multi-ethnic samples of placental RNAseq (N ~600) across several international cohorts.

2.     The relationships between paternal pre- and perinatal factors on pregnancy and offspring health.

·         -Omics data (triad (maternal, paternal, child) genomics, epigenomics, and placental RNAseq) will be used to narrow down on windows of paternal influence in the context of supporting potential interventions: e.g. pre-conception, maternal support in pregnancy, postnatal caretaking behaviors

·         This work will support an interdisciplinary and international translational science team

The Fellow will not have teaching responsibilities and will be expected only to conduct research and knowledge dissemination activities (i.e. data analyses, manuscript writing, grant writing, conference presentations, lectures, workshops, scientific community engagement, etc.).

Qualification & Field of Study

PhD in quantitative biomedical discipline e.g. Epidemiology, Biostatistics, Statistics, Bioinformatics, with experience in human cohort studies and multi-omics.

Min. Years of Experience

3 years of relevant research, including concurrent with doctorate

Other Requirements (e.g. Skills, Competencies)

Required:

·         Proficiency in statistical programming languages (R, Stata, Python)

·         Strong knowledge of biostatistics and bioinformatics

·         Strong data and analytic visualization skills

·         Knowledge of molecular epidemiologic principles including some familiarity with data collection methods and laboratory assays

·         Knowledge of epidemiologic and longitudinal research designs

·         Organized, detail oriented, able to communicate scientific concepts clearly

 Preferred:

·         Knowledge of genetics, -omics, and general physiology a plus

·         Commitment to transparent and reproducible research