Postdoctoral Fellowship Full-time

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. 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 postdocs spanning scientific disciplines to lead this advance through team science challenge problems 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. Specific labs and opportunities are available at the Convergence Fellowship Webpage, and include cutting-edge convergence researchers, data science education, and infrastructure projects.

Requirements

• Develop an independent research project in the advancement of novel data science, genomics, or profiling technologies for cancer research.
• Participate in applied research under the supervision of a multidisciplinary mentoring team.
• Provide and participate in didactic training in new technologies, including joint presentation with clinical fellows in the Oncology journal club.
• Participate in programmatic activities, presenting on research in the SKCCC trainee research activities and JHU Convergence Institute Symposium.
• Collaborate with fellow trainees in cohort on cutting-edge technologies and computational advances to solve grand challenge problems in cancer biology and clinical oncology.
• Attend training in grant writing, and submit an F32, K99/R00, or equivalent.

Open Positions

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.

Qualifications and Background

PhD or equivalent in computational biology, bioinformatics, computer science, biomedical engineering, statistics, applied mathematics, or related field with commitment to the application of emerging technologies and quantitative sciences to basic and translational cancer research.

Essential Skills:

• Programming skills in Python and R
• Background in computational or quantitative methods for biological data analysis
• Ability to work in collaborative, interdisciplinary research environments

Desired Experience (at least one area):

• Extensive programming experience and software development capabilities
• Spatial multi-omics data analysis (transcriptomics, proteomics, genomics)
• Image segmentation and processing for biological applications
• Deep learning approaches for spatial pattern recognition
• Network inference and systems biology modeling
• Bayesian and approximate Bayesian computational methods
• Pan-cancer bioinformatics analysis

Candidates with predominantly biological backgrounds who demonstrate strong quantitative aptitude and are seeking advanced training in computation and technology development will also be considered. The ideal candidate will blend computational expertise with a deep interest in cancer biology and a commitment to developing innovative methods for precision oncology.

Application Instructions

A complete application should include:

• Cover letter including description of lab and project(s) of interest listed on the Convergence Fellowship webpage.

• Current CV

• Three letters of recommendation

The Johns Hopkins University is an Equal Opportunity/Affirmative Action employer. We strongly encourage qualified women and underrepresented minorities to apply.

Tagged as: Computer Science, Data Science, Engineering, Mathematics