Xing Lab Post Doctoral AssociateUniversity of Pittsburgh

Xing Lab Post Doctoral Associate
Med-Computational and Systems Biology - Pennsylvania-Pittsburgh - (26000043)

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. In this position, the researcher will: 1) apply existing computational approaches including what developed in the Xing lab to perform mechanistic studies on single cell and spatial genomics data; 2) come up with own research questions/ideas along the general research interest of the lab and discuss with Dr Xing; 3) collaborate with the Xing lab members and external collaborators to carry the planned research on constructing biophysical virtual cell models to investigate the basic principles of gene regulation and cell fate decision, and disseminate your research finding to the scientific community in a timely manner; 4) help Dr Xing on preparing for grant proposals.

The trainee is encouraged to work with Dr Xing on applying for fellowships for career development. Guided by Professor Xing and the formal postdoc mentoring program required by the University of Pittsburgh, a career plan will be developed to prescribes a series of meeting, written plans and evaluations.

Degree requirements include a PhD in biomedical research or a related area. We are especially looking for applicants with background in dynamics inference from data, single cell genomics data analyses, and/or mechanistic studies of cellular processes. 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.

For the application, please also send your CV and a research plan to xing1@pitt.edu.

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).

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.

PI281844326