Job Description
Xing Lab Post Doctoral Associate at University of Pittsburgh Xing Lab Post Doctoral Associate at University of Pittsburgh in Pittsburgh, Pennsylvania Posted in 6 days ago.
Type:
Full-Time Job Description:
Xing Lab Post Doctoral Associate Med-Computational and Systems Biology - Pennsylvania-Pittsburgh - (26003495) It emerges as an exciting new field both in quantitative biology and computational biology on studying how eukaryotic cells make cell fate decisions and convert between different cell types by integrating big data analyses and mechanistic studies1. My lab has been focusing on putting single cell high throughput (e.g., sequencing and imaging) data analyses within the framework of mechanistic modeling2-8. The Xing Lab has an immediate opening for a highly motivated researcher at the postdoc level. The ideal candidate will have a PhD in biological physics, quantitative biology, bioengineering, mathematical biology, or a related field, with demonstrated expertise in using dynamical systems theories, and/or machine learning/AI approaches for studying biological processes through single cell genomics data analyses, and/or mechanistic studies of cellular processes. The researcher will collaborate with other lab members and external collaborators at Harvard, UCLA, UPitt, etc. Competitive candidates for the postdoc are expected to: 1) Possess experience in single-cell omics data analysis; 2) Bring additional expertise in AI/ML or dynamical systems theory-based modeling, or be willing to collaborate closely with lab members who have these backgrounds; 3) Demonstrate strong motivation and enthusiasm for learning new skills; 4) Show a record of productivity, including first-author publications. Preference will be given to applicant expected to receive a PhD degree within half a year or have received within 1-2 years. After applying, please send CV and a research plan to . After initial screening, I may ask three reference letters arranged to be sent to me directly. 1. Xing, J. Reconstructing data-driven governing equations for cell phenotypic transitions: integration of data science and systems biology. Physical Biology 19 , 061001 (2022). 2. Qiu, X. et al. Mapping Transcriptomic Vector Fields of Single Cells. Cell 185 , 690-711 (2022). 3. Hu, S. et al. Epithelial-mesenchymal transition couples with cell cycle arrest at various stages. bioRxiv , 2025.02.24.639880 (2025). 4. Zachary, R.H. et al. Dynamical modeling reveals RNA decay mediates the effect of matrix stiffness on aged muscle stem cell fate. bioRxiv , 2023.02.24.529950 (2023). 5. Chen, Y. et al. GraphVelo allows for accurate inference of multimodal velocities and molecular mechanisms for single cells. Nat Commun 16 , 7831 (2025). 6. Wang, W. et al. Live-cell imaging and analysis reveal cell phenotypic transition dynamics inherently missing in snapshot data. Science Advances 6 , eaba9319 (2020). 7. Wang, W., Ni, K., Poe, D. & Xing, J. Transiently Increased Coordination in Gene Regulation During Cell Phenotypic Transitions. PRX Life 2 , 043009 (2024). 8. Wang, W., Poe, D., Yang, Y., Hyatt, T. & Xing, J. Epithelial-to-mesenchymal transition proceeds through directional destabilization of multidimensional attractor. eLife 11 , e74866 (2022). n/a The University of Pittsburgh is an equal opportunity employer / disability / veteran. Assignment Category:
Full-time regular Campus:
Pittsburgh Child Protection Clearances:
Not Applicable Required Attachments:
Cover Letter, Curriculum Vitae Assignment Category Full-time regular Equal employment opportunity, including veterans and individuals with disabilities. PI285064851
