We are seeking recent Ph.D. graduates to join a postdoctoral fellowship program developing computational biology approaches for lung diseases. The fellowship program is jointly advised by faculty at the Institute for Computational Medicine (ICM) at Johns Hopkins University and by researchers at the United Therapeutics Corporation Computational Lab for In Silico Molecular Biology. This unique opportunity will allow postdoctoral fellows to gain industry and academic research experience working on pharmaceutical R&D projects.
Successful candidates will have an interest in improving computational modeling of biological systems. We are using multiscale mechanistic modeling to study the normal function and pathophysiology of the lung at scales ranging from organ level down to cellular and molecular level. With mentorship from Johns Hopkins and United Therapeutics, projects for postdoctoral fellows will involve applying a wide range of techniques to formulate computational models based on experimental data. These models will be used to understand the key drivers that govern system behavior and to explore potential therapeutic approaches. Example projects could include:
• Apply quantitative systems pharmacology (QSP) approaches to support model-based decisions around dosing, biomarker development, and/or combination therapies for lung diseases.
• Expand mechanistic models of lung biology using preclinical datasets to explore biological hypotheses and novel pharmacological approaches.
• Build pipelines to leverage patient data (e.g., transcriptomic, imaging, and/or hemodynamics) to augment traditional QSP workflows.
The fellowship provides an annual salary of $65,000 with full healthcare benefits. Postdoctoral scientists will be employees of Johns Hopkins University.
• Ph.D. in biomedical engineering, computational biology, or related field of study
• Highly motivated, collaborative researcher with a strong publication record
• Expertise in techniques for mathematical modeling of biological systems, including ODE/PDE-based, stochastic, and/or rule-based methods
Please email CV and cover letter to the contact emails listed above.