Research Assistant/Associate, Data Science/Risk modelling/Statistical genetics Full-time

Research Assistant/Research Associate

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 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.

We are seeking highly motivated individuals with a strong interest in cancer to join the research team under Assoc Prof Joanne Ngeow Yuen Yie. The Research Assistant/Research Associate will play a key role in technical and scientific work of unifying large scale genomic (WGS, GWAS, long read) and non genomic (EHR, lifestyle, clinical) data from multiple Asian cohorts. The position focuses on data harmonisation, statistical genetics, and developing and validating machine learning cancer risk models.

Key Responsibilities:

• Data harmonization – Ingestion, clean and merge heterogeneous genomic and phenotypic datasets
• Statistical genetics — Perform GWAS/WGS analyses, polygenic score construction
• Collaborative leadership — Liaise with international partners to coordinate data sharing and meta analysis efforts
• ML – Develop and validate cancer risk models
• Manuscripts and grants – Contribute to scientific publications, presentations, and grant applications.

Key Competencies and Requirements:

• Bachelor or Master degree in Bioinformatics, Computational Biology, Data Science
• Proficiency in Python, R, or other data science languages
• Hands on experience with variant calling/QC, GWAS, polygenic score or risk model development
• Knowledge of cloud computing, and high-performance computing (HPC) environments
• Strong ability to present findings and collaborate across disciplines