Postdoctoral Fellows in AI and Computational Biology

Overview
Postdoctoral Fellows in AI and Computational Biology — National University of Singapore
The
Yang Zhang Lab , jointly based in the School of Computing and the Yong Loo Lin School of Medicine at the
National University of Singapore , invites applications for
two Postdoctoral Fellow positions
at the intersection of
artificial intelligence (AI)
and
computational biology/medicine . Our group develops next-generation AI algorithms and foundation-model strategies for
protein and RNA structure prediction ,
de novo protein/peptide design ,
protein–ligand docking and virtual screening , and
AI-driven oncology therapeutics . The emphasis is on
methodological innovation and algorithmic creativity , rather than the routine application of existing tools.
What We Offer
Internationally competitive stipend and benefits , commensurate with experience.
Ample research resources , including generous consumables, publication support, and conference travel funding.
Cutting-edge compute power : shared access to HPC and NVIDIA H100 GPU clusters, high-memory nodes, and modern ML tooling (PyTorch/JAX, experiment tracking, MLOps pipelines).
Strong mentorship and visibility , with opportunities for first-/co-first-author publications in top-tier venues, conference presentations, and support for fellowship or grant applications.
Frontline research in AI for Science : join pioneering projects in protein/RNA modeling, molecular design, and AI-driven therapeutics within a vibrant interdisciplinary environment.
Collaborative ecosystem
across the NUS School of Computing, Yong Loo Lin School of Medicine, and Cancer Science Institute, with access to wet-lab and clinical collaborators.
Outstanding location : Singapore — a safe, global, English-speaking hub with excellent connectivity and quality of life.
Responsibilities
Lead independent research projects
in one or more of the following areas:
Foundation models and deep learning for biological sequences and structures
Protein and RNA 3D structure prediction and dynamics modeling
De novo protein/peptide design and sequence-to-function modeling
Molecular docking, virtual screening, and generative design for drug discovery
Develop and implement novel algorithms and reproducible code , curate datasets, and conduct large-scale training on GPU clusters.
Collaborate in an interdisciplinary environment , mentor junior researchers and students, and disseminate results through high-impact publications and presentations.
Required Qualifications
PhD (completed or close to completion)
in
Computer Science, AI/ML, Computational Biology, Biophysics , or a related field.
Strong programming skills
(e.g.,
Python, C/C++, scientific computing ) and practical experience with deep learning frameworks such as
PyTorch, JAX, or TensorFlow .
Demonstrated ability in algorithm/model development
and a solid publication record, including
first-author papers
in leading journals or conferences.
Excellent communication skills
and a strong commitment to
interdisciplinary teamwork .
Preferred (Nice to have)
Background in structural modeling, bioinformatics, or cheminformatics .
Experience with large-scale model training, distributed computing, or GPU optimization .
Prior research in protein/RNA modeling, molecular docking, or generative design .
Equal Opportunity
The
National University of Singapore (NUS)
is an equal opportunity employer dedicated to fostering diversity and inclusion. We welcome applications from all qualified individuals regardless of nationality, gender, age, cultural background, or other personal characteristics.
Lab & Program Information
The
Zhang Lab
is affiliated with the
NUS School of Computing , the
Yong Loo Lin School of Medicine , and the
Cancer Science Institute of Singapore . Learn more about our research.
Application Instructions
Please submit a
single PDF
to
Prof. Yang Zhang
( ) containing:
1.
CV
with a complete publication list
2.
Research statement (2–3 pages)
outlining prior work and
future research plan
3.
Contact information for three referees
Review will begin
immediately
and continue
until the positions are filled .
Start dates are flexible .
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