Research Fellow, Population and Global Health LKCMedicine

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.

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, and consists of whole genome sequences, comprehensive behavioral, phenotypic, and multi-omic data, and electronic health record linkage. Additionally, proteomic profiles of all 100,000 participants are being generated to create a state-of-the art dataset 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.

LKCMedicine is seeking to appoint a full-time Research Fellow to work as part of the Proteomics Team within the larger SG100K Data Science Team. The appointee will have the opportunity to work on this Flagship Proteomics Project alongside an interdisciplinary team of epidemiologists, statistical geneticists, and computational biologists led by Research Asst. Prof. Nilanjana Sadhu.

This role involves quality control and analysis of large-scale proteomic data generated using affinity-based and mass-spectrometry-based methods. The Research Fellow is expected to leverage complex proteomic datasets to advance scientific understanding of underlying biological mechanisms driving disease risk and progression in Asian populations.

The appointee will play a key role in supporting both Professor Chambers' and the team, mainly in, but not limited to, the following roles:

• Developing and implementing pipelines for quality control and analysis of data.

• Conducting regular literature review to stay up-to-date on best practices.

• Leading and contributing to the writing of publications in peer-reviewed journals, and presenting at international conferences.

• Critically reviewing the work of other members of the research team and mentoring less experienced members as necessary.

Key Competencies and Requirements:

• PhD in a relevant area (e.g. molecular epidemiology, bioinformatics, computational biology). Candidates who have successfully defended their thesis or dissertation are welcome, subject to evaluation on a case-by-case basis.

• Experience analyzing large-scale population data.

• Experience analyzing affinity-based (e.g., SomaScan and Olink) and mass-spectrometry-based (Data Independent Acquisition) proteomic data.

• Understanding of statistical methods commonly used in genetics and molecular epidemiology.

• Expertise in R, Python, bash is required.

• Strong communication (oral and written) and interpersonal skills, and experience working in a large research group is desirable.

• Fluent in spoken and written English.

• Ability to work independently and collaboratively, with a pro-active and motivated mindset, attention to detail.