Computational Scientist

Overview

Computational Scientist - AI & Data Integration (Metabolic Disease), A*STAR BII

Join to apply for the Computational Scientist - AI & Data Integration (Metabolic Disease) role at A*STAR - Agency for Science, Technology and Research. The Research Data Integration group at the Bioinformatics Institute (BII), A*STAR, aims to bridge computational biology and data science with the hypotheses and findings of biologists and clinicians. The successful candidate will develop AI/ML frameworks to integrate and analyze high dimensional biological datasets (e.g., genomics, transcriptomics, proteomics, imaging) with clinical data to identify novel mechanisms, biomarkers, and therapeutic targets in metabolic diseases such as MASLD/MASH and obesity.

The position offers the opportunity to collaborate with a diverse team of computational and experimental scientists, clinicians, and other stakeholders to push the potential of biomedical data and drive translational impact that benefits patients.

• Develop and implement AI/ML models to analyze and integrate multi-omics and clinical datasets related to metabolic disorders.
• Collaborate with biologists and clinicians to generate testable hypotheses from integrated datasets, and contribute to laboratory validations and screening studies through analytical findings.
• Design pipelines for preprocessing, normalization, and harmonization of heterogeneous data types.
• Apply and develop novel computational methodologies to uncover disease mechanisms.
• Drive biomarker discovery, patient stratification, and target identification using machine learning approaches.
• Build a database integrating and linking multi-dimensional clinical and multi-omics data for metabolic diseases.
• Develop visualizations and dashboards to communicate complex data insights to interdisciplinary teams.
• Stay up to date with emerging computational techniques and tools in systems biology and AI.
• Publish high-impact scientific papers, prepare patent filings, presentations and grant proposals.

• Ph.D. in Computational Biology, Bioinformatics, Computer Science, Data Science, Systems Biology, or a related field.
• Strong background in programming and AI/ML (e.g., deep learning, ensemble methods, graph-based learning, explainable AI).
• Proficiency in Python and R; experience with AI/ML frameworks like TensorFlow, PyTorch, or scikit-learn.
• Demonstrated experience in integrating and analyzing complex multi-omics datasets (e.g., RNA-seq, WGS, proteomics, GWAS).
• Experience with Unix/Linux environments and/or cloud architecture.
• Solid understanding of metabolic disease biology and relevant clinical phenotypes.
• Experience working with large-scale multi-dimensional datasets from biobanks, cohorts, or clinical trials.
• Track record of peer-reviewed publications in computational biology or bioinformatics.
• Experience in a cross-functional, collaborative environment in academia or industry.
• Knowledge of data security, data standards and interoperability, and reproducible research practices.
• Strong analytical and problem-solving skills, and attention to detail.
• Excellent oral and written communication and presentation skills.
• Able to work independently and collaboratively in a multi-disciplinary team environment.
• Competent in project and data management, and organizational skills.

• Associate

• Full-time

• Research, Analyst, and Information Technology
• Industries: Research Services

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