The Tang Lab at the University of British Columbia is recruiting multiple postdoctoral fellows and PhD students to advance research in AI for Life Sciences, Computational Neuroscience, and Biomedical Research. The lab focuses on developing interpretable and autonomous AI for multi-scale and multi-modal spatiotemporal biology, including single-cell, spatial, and multi-omics data, with applications in neuroscience, brain-computer interfaces, and cancer genomics. The research environment is highly collaborative, diverse, and stimulating, with access to high-performance GPUs and rich datasets. Three postdoctoral positions are available: (1) AI for Biology, focusing on next-generation computational and machine-learning frameworks for single-cell, spatial, and multi-modal omics; (2) Computational Neuroscience and Multimodal AI, emphasizing large-scale neural recording, multimodal representation learning, and brain-inspired AI; and (3) Reinforcement Learning and Autonomous Laboratory Systems, developing decision-making algorithms for autonomous laboratory platforms. Expertise in deep learning, representation learning, uncertainty quantification, computer vision, reinforcement learning, robotics, and explainable AI is sought. A biomedical background is not required for any position. PhD positions are open to students interested in developing AI and machine learning methods or applying AI to biomedical research. Applicants should have a strong programming and AI background; exceptional students without biological experience are considered. The lab recruits from several UBC graduate programs, including Computer Science, Genome Science + Technology, and Bioinformatics. The application deadline for graduate programs is December 15, while postdoctoral applications are accepted until February 15, 2026. Funding for these positions is provided by prestigious grants such as the NSERC Discovery Grant, Canada Research Chair Tier 2, and the John R. Evans Leaders Fund from CFI. The lab is located in Vancouver, offering a vibrant city life alongside natural beauty. Interested candidates should email Prof. Xin Tang with their CV and a brief description of research interests. For postdoctoral roles, include a GitHub account, a one-page research highlights letter, and contact information for three references. Applications are reviewed on a rolling basis, and formal applications can be submitted via the lab website. For more information, visit Tang Lab and Join Us . The lab welcomes undergraduate, master's, and visiting scholars, with preference for those with AI/ML coursework and commitment to long-term research involvement.

The University of British Columbia (UBC) is recruiting postdoctoral fellows for interdisciplinary research in AI × Life Sciences, with a focus on AI for biology, computational neuroscience, and reinforcement learning for autonomous laboratory systems. These positions are hosted in the Tang Lab, Michael Smith Laboratories, and the Department of Computer Science. Fellows will develop interpretable and autonomous AI for multi-scale and multi-modal spatiotemporal biology, working closely with experimental groups and leveraging high-performance GPUs and rich datasets. The research areas include next-generation computational and machine-learning frameworks for single-cell, spatial, and multi-modal omics, uncertainty quantification, interpretable and biologically plausible modeling, and foundational AI tools for biological discovery in cancer, regenerative medicine, and neuroscience. Additional opportunities exist in computational neuroscience, focusing on multi-modal data integration, neural circuit modeling, and biologically grounded representation learning. Another track involves reinforcement learning and decision-making algorithms for autonomous laboratory platforms, including closed-loop experimental design and integration of VLA models with real-world laboratory systems. Applicants should have expertise in deep learning, representation learning, multi-modal learning, generative modeling, uncertainty quantification, computer vision, 3D modeling, video processing, multi-view learning, point clouds, reinforcement learning, control, active learning for experimental design, multi-agent learning, robotics, VLA models, interpretable and explainable machine learning, mechanistic interpretability, and domain experience in single-cell and spatial omics, neuroscience, or cancer genomics. A biomedical background is not required. The positions offer strong support for high-impact publications and collaboration with interdisciplinary teams. For more information and to apply, visit the Tang Lab website or email Prof. Xin Tang with your CV and research interests. Applications are reviewed on a rolling basis.