Scientist (Drug Discovery), BII

A*STAR - Agency for Science, Technology and Research; Singapore; On-site; SGD 80,000 - 110,000; Full time

Job summary

A prominent research agency in Singapore is seeking a passionate Computational Scientist to bridge methodological innovation and real-world impacts in drug discovery. You will design innovative computational approaches and collaborate with experts in the field, ensuring transformative advancements in R&D. The role requires a PhD in relevant fields and a solid background in AI/ML methodologies. This position offers a unique opportunity to lead research initiatives and mentor junior colleagues in a dynamic environment.

Qualifications

• PhD in AI, Computer Science, Bioinformatics, or related fields with focus on machine learning.
• Strong publication record or contributions to open-source tools.
• Expertise with AI/ML methodologies and implementations.

Responsibilities

• Design and deploy innovative computational approaches for drug R&D.
• Develop frameworks integrating diverse data types into cohesive models.
• Collaborate with experts in drug discovery to develop computational models.
• Publish findings and contribute to strategic initiatives.
• Mentor junior team members in a collaborative environment.

Skills

AI/ML methodologies

Computational modeling

Problem-solving skills

Collaborative research

Education

PhD in relevant field

Job description

Overview

Do you want to bridge the gap between methodological innovation and real impacts in drug discovery?

We are seeking a passionate Computational Scientist to join our interdisciplinary research team focused on the integration of Computation, AI/Machine Learning, and Drug Discovery. You are a PhD-level researcher with a strong track record in developing novel computational methodologies and motivated by the research that make transformative impacts on how medicines are discovered and developed.

Responsibilities

• Design and deploy innovative computational approaches — integrating physics-informed, biology-informed, causal and uncertainty-aware machine learning — to accelerate and de-risk key stages of drug R&D, including target/biomarker identification, molecular optimization, translational predictive modeling.
• Develop and optimize computational frameworks that integrate diverse data types (chemical, biological, omics, clinical) into cohesive models.
• Collaborate with domain experts in computational biology, cheminformatics, pharmacology, and drug discovery to tailor computational models to real-world problems.
• Publish research findings in leading journals and conferences, and contribute to partnerships and strategic initiatives as opportunities arise.
• Mentor junior team members and contribute to a collaborative, cross-disciplinary research environment.

Requirements

• PhD in Artificial Intelligence/Computer Science, Bioinformatics, Computational Biology, Biomedical Engineering, Applied Mathematics, Pharmaceutical Sciences or a related field, with a focus on machine learning or computational modeling.
• Strong publication record or demonstrable contributions to open-source tools or reproducible research.
• Demonstrated expertise with AI/ML methodologies and implementations.
• Excellent problem-solving skills, with an ability to balance theoretical rigor with practical implementation.

Preferred Qualifications

• Familiarity with challenges in drug discovery and development.
• Research interest in areas of AI/ML such as Multi-Agent Systems, Physics-Informed ML, Causal AI, Neuro-Symbolic AI, Uncertainty Quantification, Active Learning, Geometric Deep Learning.