Postdoc in AI for Biomolecular Modeling & Drug Discovery

Computational Chemistry List Home; Singapore; On-site; SGD 60,000 - 90,000; Full time

Benefits offered by this job

Internationally competitive salary and

Strong logistical and research support

Singapore-based position

Job summary

Yang Zhang Lab at the National University of Singapore (NUS) is recruiting two postdoctoral fellows in AI and computational biology to advance biomolecular modeling and AI-driven drug discovery, including protein and RNA folding, structure prediction, sequence design, and modeling of biomolecular states and interactions.

The work is research‑driven with emphasis on original algorithm and model development, including foundation‑model architectures that integrate sequence, structure and physical

Qualifications

• PhD in computer science, ML, biophysics, computational biology, computational chemistry, or closely related field (or near completion).
• Strong programming skills and the ability to develop new models or methods.
• Strong publication record in relevant areas.
• Experience in structural bioinformatics, molecular modeling, AI-driven drug discovery, protein/RNA modeling, or large-scale deep learning is a plus.
• Methodological depth, research creativity, and the ability to independently lead research projects.

Responsibilities

• Develop AI methods for biomolecular modeling and drug discovery.
• Lead projects from algorithmic conception through implementation, large‑scale training, and rigorous evaluation.
• Prepare manuscripts and lead publications.
• Collaborate across computer science, structural biology, and biomedical research.
• Utilize high‑performance computing systems, including hybrid NVIDIA GPU/CPU clusters.

Skills

Programming

Publication record

Independent leadership

Cross-disciplinary collaboration

Education

PhD in related field

Tools

HPC clusters

Large-scale deep learning

Job description

Yang Zhang Lab at the National University of Singapore (NUS) is recruiting two postdoctoral fellows in AI and computational biology to advance biomolecular modeling and AI-driven drug discovery, including protein and RNA folding, structure prediction, sequence design, and modeling of biomolecular states and interactions.

The work is research‑driven with emphasis on original algorithm and model development, including foundation‑model architectures that integrate sequence, structure and physical