Research Associate

at  Yong Loo Lin School of Medicine

Posting Start Date: 25/11/2024
Job Description:

JOB DESCRIPTION

Join our physician-led cutting-edge metabolomics lab as a Bioinformatics Expert and help us make an impact in the world of precision medicine.
We are a dynamic research lab at the forefront of metabolomics, dedicated to advancing knowledge in metabolic pathways, biomarker discovery, and personalized healthcare. Our interdisciplinary team collaborates with both Singapore based and global institutions. We offer opportunity to work on innovative projects with a collaborative team and a patient-centric focus with access to state-of-the-art facilities and resources.
Main Duties and Responsibilities

QUALIFICATIONS

The applicant Must:

• Master’s in Bioinformatics, Computational Biology, or related field.
• Proficiency in R, Python, and/or bioinformatics tools for metabolomics.
• Ability to build custom applets/scripts for data analysis.
• Ability to thoroughly explore large data sets (up to ~PB scale), derive hypotheses, and adjust analysis dynamically.
• Experience in database query (e.g. KEGG, Ensemble, HMDB, wikipathway).
• Awareness of data standards for clinical research and statistical analysis (e.g. EQUATOR Network)
• Strong analytical and communication skills.
• Experience in and/or knowledge of medical data, clinical processes or trials
• Experience in constructing clinical risk prediction models for biomarker research will be a plus.
• Experience in translating conceptual algorithms to implementable code will be a plus.
• At least 2 years of relevant research experience and prior publications will be preferred.

• Develop and implement data extract, transform, and load (ETL) pipelines for metabolomics data (mainly LC-MS/MS based metabolomics).
• Perform data integration from multi-omics platforms (genomics, transcriptomics, proteomics).
• Apply statistical and machine learning techniques (preferably explainable models) for biomarker discovery in large clinical dataset.
• Maintain and update databases and in-house analytical tools for metabolite identification and pathway analysis.
• Lead or significantly contribute to successful collaboration in clinical cohort research.
• Derive clinically actionable insights from data patterns.
• Collaborate with biologists, chemists, and clinicians to interpret results and publish findings.