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, the Yong Loo Lin School of Medicine, and the Cancer Science Institute of Singapore 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 (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 at: Application Instructions Please submit a single PDF to Prof. Yang Zhang ( ) containing: CV with a complete publication list Research statement (2–3 pages) outlining prior work and future research plan Contact information for three referees Review will begin immediately and continue until the positions are filled. Start dates are flexible. #J-18808-Ljbffr