Scientist, Drug Discovery (A*STAR VII)

Job Summary

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.
  

REQUIREMENT

• 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.

The above eligibility criteria are not exhaustive. A*STAR may include additional selection criteria based on its prevailing recruitment policies. These policies may be amended from time to time without notice. We regret that only shortlisted candidates will be notified.