Computational Scientist – AI & Data Integration (Metabolic Disease), BII Full-time

Computational Scientist – Ai & Data Integration (Metabolic Disease), BII

AI, machine learning, and data science are transforming biomedical and translational research, where large-scale, multi-modal data are being generated at unprecedented scale. The Research Data Integration Group at the Bioinformatics Institute (BII), A*STAR, bridges computational biology, data science, and clinical research to drive discovery and translational impact.

One of our key challenges is to integrate and analyze multi-omics, imaging, and clinical datasets generated by A*STAR institutes, healthcare partners, and national initiatives in Singapore, focusing on cancer, metabolic, eye, and skin diseases. We seek motivated individuals to harness the potential of biomedical data, build intelligent agentic AI systems, and develop novel digital tools that accelerate biological discoveries and improve patient outcomes via therapeutic discoveries and precision medicine strategies.

Position summary: We are seeking a highly motivated Computational Scientist with expertise in artificial intelligence, multi-omics data integration, and metabolic disease biology. The successful candidate will develop agentic AI frameworks and intelligent chatbots to automate data analysis and interpretation, integrate high-dimensional datasets (genomics, transcriptomics, proteomics, imaging, drug screening) with clinical data, and enhance scientific collaboration.

This role offers a unique opportunity to work at the interface of AI, biomedical research, and clinical translation, collaborating with computational scientists, experimental biologists, and clinicians to uncover mechanisms, biomarkers, and therapeutic targets in metabolic diseases such as MASLD/MASH and obesity.

Key Responsibilities

• Develop and implement AI/ML and agentic AI systems to analyze and integrate experimental/multi-omics and clinical datasets related to metabolic diseases.
• Leverage agentic AI for automated hypothesis generation, exploratory data analysis, and prioritization of candidate mechanisms, biomarkers, and therapeutic targets.
• Design and deploy conversational AI chatbots and knowledge assistants to enable intuitive access to complex datasets and analytical workflows.
• Collaborate with biologists and clinicians to refine AI-driven hypotheses into testable biological studies and validations.
• Build scalable data pipelines for preprocessing, harmonization, and integration of heterogeneous omics and clinical data.
• Apply and innovate computational methodologies to uncover disease mechanisms.
• Drive therapeutic target identification, therapeutic strategies development, and patient stratification using machine learning and AI-driven inference.
• Build databases and interactive dashboards integrating multi-dimensional omics and clinical data with AI insights.
• Build a resource of public datasets and knowledgebase for metabolic diseases to be integrated with in-house proprietary data.
• Stay up to date with emerging computational and agentic AI technologies relevant to systems biology, biomedical informatics, and translational medicine.
• Collaborate with industry partners and start-ups to accelerate agentic AI technologies development.
• Contribute to high-impact scientific publications, grant proposals, and patent filings.

Qualifications and Preferred Experience

• Ph.D. in Computational Biology, Bioinformatics, Computer Science, Data Science, Systems Biology, or a related field.
• Strong foundation in AI/ML, including deep learning, ensemble methods, graph-based learning, agentic AI, and explainable AI.
• Proven experience in developing or integrating conversational AI/chatbots (e.g., LLM-based assistants, RAG systems, domain-specific AI agents).
• Demonstrated experience in multi-omics data integration and analysis (e.g., RNA-seq, WGS, proteomics, GWAS, pheWAS, drug screens).
• Proficiency in Python and R, with hands-on experience using TensorFlow, PyTorch, or scikit-learn.
• Understanding of metabolic disease biology and relevant clinical phenotypes.
• Experience working with large-scale, multi-dimensional datasets from biobanks, cohorts, or clinical trials.
• Proficiency in Unix/Linux environments and cloud or HPC architecture.
• Track record of peer-reviewed publications in computational biology, bioinformatics, or AI.
• Strong analytical, communication, and organizational skills, with ability to work collaboratively in multidisciplinary teams.
• Knowledge of data security, FAIR principles, and reproducible research practices.
• Experience in a cross-functional, collaborative environment in academia or industry.
• Strong analytical and problem-solving skills, and attention to details.
• Excellent oral and written communication and presentation skills.
• Able to work independently and work collaboratively in a multi-disciplinary team environment.
• Competent project and data management, and organizational skills.