Research Fellow, Population and Global Health

The Lee Kong Chian School of Medicine (LKCMedicine) trains doctors who put patients at the centre of their exemplary care. The School, which offers both undergraduate and graduate programmes, is named after local philanthropist Tan Sri Dato Lee Kong Chian. Established in 2010 by Nanyang Technological University, Singapore, in partnership with Imperial College London, LKCMedicine aims to be a model for innovative medical education and a centre for transformative research. The School’s primary clinical partner is the National Healthcare Group, a leader in public healthcare recognised for the quality of its medical expertise, facilities and teaching. The School is transitioning to an NTU medical school ahead of the 2028 successful conclusion of the NTU-Imperial partnership to set up a Joint Medical School. In August 2024, we welcomed our first intake of the NTU MBBS programme, that has been recently enhanced to include themes like precision medicine and Artificial Intelligence (AI) in healthcare, with an expanded scope in the medical humanities. Graduates from the five-year undergraduate medical degree programme will have a strong understanding of the scientific basis of medicine, with an emphasis on technology, data science and the humanities.

LKCMedicine is seeking to appoint a full-time Research Fellow to work on the IN-CYPHER Programme. IN-CYPHER is Imperial Global Singapore’s inaugural research programme in collaboration with NTU, which brings together complementary expertise from Imperial College London and NTU to tackle existing security challenges to protect emerging sensing technologies and their data from being compromised. Within the IN-CYPHER programme, NTU LKCMedicine is leading research in clinical innovation and translation built upon the infrastructure of SG100K population cohort study.

SG100K, spearheaded by Prof. John Chambers, is a landmark population cohort study of 100,000 Singaporeans of diverse ethnic background. The cohort has completed recruitment of >80,000 participants at the point of this posting. With comprehensive behavioral, phenotypic, and multi-omic data, and electronic health record linkage enriched baseline phenotyping and evaluation of prospective outcomes, the study represents a state-of-the art platform for researchers to understand the etiology and pathogenesis of diverse disease outcomes in Asia, and to generate insights that have the potential to improve health outcomes for Asian populations globally.

It is well appreciated that the classification of diabetes into predominant types 1 and 2 inadequately captures the heterogeneity in symptom presentations, response to therapy, disease progression and complication, and that further subclassification of diabetes into more homogeneous groups offers opportunities for tailored and targeted early treatment and precision medicine in diabetes care. The initial project will aim at assessing the reproducibility of five subtypes of diabetes identified in a north European population with different trajectories of disease progression and complications. Then, using extensive clinical characterization and rich collection of biological samples in the SG100K cohort, the project will evaluate whether additional phenotypic and genotypic traits can be used to refine clinical subtypes of diabetes in a multi-ethnic cohort of participants with established and newly diagnosed diabetes in Singapore.

The appointee will have a unique opportunity to work with a leading interdisciplinary team of epidemiologists, clinicians, scientists, and engineers to explore the clinical innovation and translation potential of increased connectivity and aggregation of data from diagnostic, therapeutic and implantable devices enabled by technologies developed by the In-Cypher programme.

The primary responsibilities include, but not limited to- Develop, implement, and validate statistical models to analyze complex datasets.- Collaborate with multidisciplinary teams to design studies and interpret results.- Prepare technical reports, publications, and presentations for academic and stakeholder audiences.- Contribute to grant proposals and project documentation as required.- Critically reviewing the work of other members of the research team and mentoring less experienced members.- As necessary, contributing to data pipeline and administration, which may include data acquisition, linkage, quality control, and project management.

Competencies and Qualification Requirements:
- PhD (or equivalent) in clinical epidemiology, statistics, clinical endocrinology.- Strong research experience in endocrinology and diabetes is desirable.- Knowledge of biological sample processing would be an advantage.- Proficiency in statistical software (e.g., R, Python, SAS, or Stata).- Experience with clinical informatics approaches (e.g., cluster analysis, machine learning, Bayesian statistics etc.) would be of advantage.- Strong communication (oral and written) and interpersonal skills