The Convergence Fellows Program

The Convergence Institute is recruiting postdoctoral fellows for The Convergence Fellows Program.

The goal of the Convergence Institute is to bring together the brightest minds in cancer biology, clinical oncology, engineering, and computational biology to make new discoveries and bring forward the next generation of cancer research. The Institute consists of a transdisciplinary team of faculty and co-mentored trainees across career stages.

Data science and new measurement technologies are revolutionizing cancer research. Formal didactic programming will be offered to promote cross-pollination of fellows between labs and the oncology medical fellowship programs to ensure the transdisciplinary training, productivity, and career development of the fellows. This collaborative, transdisciplinary research environment, promoted by the program and the broader Johns Hopkins University, fosters a diverse and inclusive community. Trainees will have the opportunity to develop hybrid wet/dry lab research skills to advance multi-disciplinary team science 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

 

  • Database Integration of Multi-Omics and Pre-Cancer Atlase (Cope and Shih Labs). The candidate will work with experts in database design, refinement, and integration of multi-omics data collected from the pre-cancer Atlas study. This highly integrated program is fundamental for building up the pre-cancer atlas to explore the earliest events in developing tumor precursors. The knowledge will have an unprecedented impact on cancer biology, early detection, diagnosis, and prevention. Specifically, the candidate will work on analyzing the single-cell omics dataset alongside other assignments as a part of the pre-cancer atlas. 

 

  • Spatial Multi-Omics and Tumor Atlases (Deshpande Lab). Develop gene and cell regulatory network inference methods and scalable software to infer mechanisms of tumor-immune interactions in large scale cancer atlases as part of the Break Through Cancer multi-institutional consortium.

 

  • Cancer Epigenetics and Early Detection (Easwaran Lab). Integrated analyses of epigenomics and single-cell omics data, aiming to innovate approaches and biomarkers for colon cancer prevention and early diagnosis. 

 

  • Tumor Forecasting (Fertig, Deshpande, and Stein-O’Brien Labs). Development and implementation of novel computational integration methods to blend mathematical modeling, artificial intelligence, and spatial multi-omics technologies. Applications of this software will include prediction of tumorigenesis, metastasis, and therapeutic response leveraging cutting edge datasets generated in the Institute and with clinical partners. 

 

  • Clonal hematopoiesis and hematologic malignancies (Gondek Lab). Utilizing single-cell omics techniques to unravel the complex mechanisms underlying the progression from clonal hematopoiesis to overt leukemia. Development of a comprehensive molecular database for hematologic malignancies at Johns Hopkins, fully integrated with clinical outcome data will also allow high-throughput clinical and research datasets to be leveraged to enhance patient care.

 

  • Tumor microenvironment profiling to predict targeted therapies in patients with gastrointestinal cancers (Jaffee, Fertig, Kagohara, Ho).  Utilizing single cell transcriptional and proteomic spatial data from patients treated on immunotherapy trials to build computational models that inform pathways of sensitivity and response to different treatments.  These models inform next-generation clinical trials.   

 

  • Tumor Evolution and Immune Interactions (Karchin Lab). Applying and developing innovative machine learning techniques for multi-modal modeling of tumor evolution and tumor-immune interactions. 

 

  • Immunometabolism of Macrophages (Sanin Lab). Integrative analysis of single-cell multi-omics and novel proteomics technologies can uncover how the metabolism of macrophages impacts their ability to persist in tissues and influence disease outcomes.

 

  • Liquid biopsies and early detection (Scharpf and Velculescu Labs). Development and application of computational and statistical methods for integrated analyses of high throughput sequencing studies, including DNA/RNA, chromatin accessibility, and methylation sequencing. Applications include the development of machine learning approaches for early detection of cancer from liquid biopsies.  

 

  • Data Science and Genomics Education (Tan and Hu Labs). The Convergence Institute is building innovative educational programs, such as the CRC Convergence program, to deliver and implement data science, technology, and genomics education to the translational cancer research community. Will support curriculum development, workshop teaching, and leadership of collaborative hands-on-training leveraging Convergence Institute datasets to develop a new model for transdisciplinary, team-science education. 

 

  • Epigenetic mechanisms in tumor progression and therapy resistance (Toska Lab). We are interested on the role that chromatin-based mechanisms play in metastasis and therapy resistance by using bulk epigenomic assays and at the single cell level (scRNA-seq and scATAC-seq) in hormone-driven cancers with the goal to identify novel combinatorial therapies.

 

  • Data Infrastructure (Quantitative Sciences Division). Research in novel tools for accessibility, storage, sharing, and automated analysis of data from cutting-edge single-cell, spatial molecular, and imaging technologies. Position will be co-mentored by leadership of the SKCCC Quantitative Sciences Division and focus on novel technologies developed in the institute and leveraged for multi-institutional consortia projects.