Postdoctoral Positions in Computational Biology, Deep Learning, and Network Systems Biology at Cornell University

NIH-funded postdoctoral positions are available in Dr. Haiyuan Yu's laboratory at Cornell University for highly motivated candidates interested in computational biology and network systems biology. The lab focuses on both high-throughput experimental (proteomics and genomics) and integrative computational (network analysis and machine learning) methodologies. Research aims to understand gene functions and their relationships within complex molecular networks, such as the protein-protein interactome and gene regulatory networks, and how perturbations to these systems may lead to various human diseases. The postdoc opportunity is centered on developing deep learning algorithms for two main research areas: (1) modeling 3D protein complex structures and assessing the impact of mutations or post-translational modifications (PTMs) on protein structure and interactions; (2) dissecting functional elements (e.g., enhancers) throughout the genome and evaluating the impact of noncoding mutations on gene expression. The ideal candidate will have a strong publication record and extensive experience in computational biology, with expertise in one or more of the following: machine learning (or deep learning), structural modeling/analysis, and genome/transcriptome analysis. Computer scientists with strong deep learning experience are encouraged to apply, even if they lack a biology background. Applicants should send a PDF with their CV, a brief description of future research interests (optional), and contact information for three references to Dr. Haiyuan Yu ([email protected]). Informal inquiries are welcome. For more information about the lab and its research, visit www.yulab.org . This is a fully funded postdoctoral position supported by NIH grants. The position offers an excellent opportunity to work at the intersection of computational biology, deep learning, genomics, and proteomics at Cornell University, a leading research institution in the United States.