Research Assistant/Associate, Data Science/ Artificial Intelligence
Research Assistant/Associate, Data Science/ Artificial Intelligence
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Job Description
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 Schools 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 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.
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
Required Experience:
IC
Employment Type
Full-Time
