The Convergence Fellows Program
The Convergence Institute is recruiting postdoctoral fellows for The Convergence Fellows Program.
Data science and new measurement technologies are revolutionizing cancer research. Formal didactic programming will be offered to promote the cross-pollination of fellows between labs and the oncology medical fellowship programs to ensure the fellows’ transdisciplinary training, productivity, and career development. This collaborative, transdisciplinary research environment, promoted by the program and the broader Johns Hopkins University, fosters a diverse and inclusive community. Additionally, it provides the opportunity to form a cohort of postdoctoral fellows working together to advance the next generation of Convergence Cancer research. Trainees will have the opportunity to develop hybrid wet/dry lab research skills to advance multi-disciplinary team science research.
The Convergence Fellows program will form a cohort of postdoctoral fellows spanning scientific disciplines to lead this advance through team science challenges in cancer research. Trainees will work at the cutting-edge of technology-driven, team-science research in cancer biology under the mentorship of Johns Hopkins University School of Medicine Sidney Kimmel Comprehensive Cancer Center (SKCCC) Investigators and large-scale team science projects advancing Convergence research, with specific opportunities as described below.
Available Fellowship Opportunities
System Identification and Model Calibration for Precision Medicine
Lead the development of a novel computational framework that synergistically combines mathematical modeling, artificial intelligence, and spatial multi-omics technologies. Apply this integrated framework to predict spatiotemporal dynamics of tumorigenesis, metastasis, and therapeutic response. Leverage cutting-edge datasets generated through institute resources and clinical partnerships to build predictive models that can inform treatment decisions. Develop innovative model calibration and uncertainty quantification methods that can account for heterogeneity in spatial data and patient-specific factors.
Pan-Cancer Analysis of Spatial Multi-Omics
Apply state-of-the-art bioinformatics methods to identify cellular niches and characterize tumor-immune microenvironments across multiple cancer types. Conduct comparative analyses to discover shared and distinct spatial patterns in the tumor microenvironment. Use advanced statistical approaches to integrate data from the Break Through Cancer multi-institutional consortium to uncover conserved mechanisms of cancer progression and therapeutic resistance. Develop visualizations and analytical tools that enable cross-cancer comparisons at multiple spatial scales.
Spatial Multi-Omics Analysis Methods and Pipelines
Develop gene and cell regulatory network inference methods to uncover mechanisms of tumor-immune interactions in large-scale cancer atlases. Create scalable software and analysis pipelines for both spot-based and subcellular resolution spatial multi-omics data. Design integrative approaches that harmonize spatial transcriptomics, proteomics, and genomics datasets within the Break Through Cancer multi-institutional consortium. Implement sophisticated computational approaches that can handle the high dimensionality and complexity of spatial data across multiple technology platforms.
Cancer Immunology and Immunotherapy
Fellows will work at the convergence of data science/ML, cancer immunology and immunotherapy clinical trials. Recent advances in multi-omics technologies have empowered cancer immunology research by generating detailed cellular and molecular maps of patients’ immune responses. Using these maps, in collaboration with clinical collaborators, fellows will aim to track lymphocytes in patients across space and time with novel technologies and computational methods to identify immunotherapy targets and mechanisms of response and resistance.