Co-op - Computational Lab

United Therapeutics•Silver Spring, MD

3d•Onsite

About The Position

United Therapeutics (UT) is seeking a Spring 2026 Co-op for its computational systems biology lab in Silver Spring, MD. This role will assist in the development and implementation of mathematical models of molecular and physiological processes that regulate lung diseases and drug response. The goal is to build an in-silico model of the lung to support drug development. This coop will run from June/July (or sooner) for 6 months.

Requirements

Candidates are expected to be actively enrolled in a Master's or PhD program in a science/engineering discipline
Available to work 40 hours/week onsite in Silver Spring, MD
Experience with implementation of modeling and simulation (either PDE-based fluid dynamics, ODE-based signaling models, or agent-based modeling) or bioinformatics / machine learning skills (project-dependent)
Highly self-motivated
Excellent teamwork skills
Strong writing and communication skills
Proficiency in at least one of the following: R, Python, Julia, Matlab, or other programming language

Nice To Haves

Simulate the complex interplay between lung tissue mechanics and fluid dynamics (either air or blood), with a focus on how mechanical alterations due to disease conditions can affect clinical measurements related to hemodynamics and pulmonary function testing
Apply statistical / machine learning approaches to analyze large-scale molecular data (transcriptomics, proteomics, etc.) to derive insights into lung function and drug response
Generate mechanistic ODE-based models of signaling pathways relevant to lung physiology and drug response, and apply quantitative systems pharmacology approaches to assist with clinical development objectives
Apply agent-based modeling to represent behavior of cell populations on tissue engineered scaffolds

Responsibilities

Conduct research on mathematical modeling approaches to add new features to the in-silico lung model; evaluate suitability for addressing research questions and feasibility of implementation
Identify data in the literature and public repositories suitable for training and validating mathematical models
Implement mathematical models by developing well-documented code using version control systems
Present plans and results regularly to members of the computational lab and other scientists within the company