Postdoctoral Fellow - AI/ML Upstream Cell Culture Modeling & Process Development

Position Summary

The Upstream Process Development group within our BR&D organization is seeking a Postdoctoral Fellow to join a team of scientists and engineers focused on developing and optimizing mammalian cell culture processes for recombinant proteins and other modalities for early- and late-phase clinical trials. This role will focus on developing and applying innovative mathematical and computational modeling approaches to characterize, understand, and predict the complex biological systems used in mammalian cell culture. The fellow will build mechanistic and data-driven models, including genome-scale and hybrid metabolic models, to predict cellular behavior, diagnose process bottlenecks, and rationally guide the design of feeding strategies and medium compositions. Where current development often relies on iterative empirical screening, this position aims to establish a model-guided approach that narrows the experimental search space before committing significant laboratory effort. The fellow will also explore the use of these models within real-time monitoring and control frameworks, and will leverage machine learning to enable earlier, model-informed decisions such as clone selection based on predicted process performance. The work establishes a closed-loop cycle in which model predictions are validated experimentally and the resulting data continuously improves model accuracy. This position is well suited to a highly motivated scientist who wants to bridge computational modeling and hands-on bioprocess experimentation.

Responsibilities

• Develop and calibrate mechanistic and hybrid metabolic models of mammalian cell culture processes, integrating process data to predict cellular behavior and identify performance-limiting factors.
• Build computational pipelines that turn routine bioprocess data into model inputs and generate predicted metabolic flux distributions across the culture cycle.
• Apply machine learning and data-driven methods for performance prediction and to integrate model-derived features with experimental data.
• Use calibrated models to evaluate feeding strategies and medium compositions to enhance productivity.
• Design and execute cell culture experiments—from shake flask to bench-scale bioreactors—to generate datasets for model development, training, and validation.
• Collaborate with the Analytical team to develop multi-omics methods for metabolic model calibration.
• Integrate model-derived features into machine learning workflows to support earlier decisions, including predictive clone selection from earlier-stage process data.
• Investigate AI-assisted approaches to accelerate model building, validation, and reuse across projects, with human-in-the-loop decision support.
• Maintain rigorous documentation, communicate results through technical reports, presentations, and peer-reviewed publications, and collaborate across cross-functional teams.

Basic Requirements

• PhD in Chemical/Biochemical Engineering, Bioengineering, Systems Biology, Computational Biology, Metabolic Engineering, or a related field.
• Strong foundation in mathematical modeling, reaction kinetics, and mammalian cell culture.
• Experience with constraint-based or genome-scale metabolic modeling.
• Hands-on experience designing and executing cell culture experiments, with a working understanding of Batch, Fed-batch, and Intensified Processes.
• Proficiency in Python, MATLAB, or similar scientific programming languages.
• Qualified applicants must be authorized to work in the United States on a full-time basis. Lilly will not provide support for or sponsor work authorization or visas for this role, including but not limited to F-1 CPT, F-1 OPT, F-1 STEM OPT, J-1, H-1B, TN, O-1, E-3, H-1B1, or L-1.

Additional Preferences

• Familiarity with process control concepts, including model predictive control or dynamic optimization.
• Understanding of mammalian (e.g., CHO) cell physiology and metabolism relevant to bioprocessing.
• Experience with analytical methods such as HPLC, UPLC, and mass spectrometry.
• Experience integrating omics data with mechanistic or hybrid models.
• Demonstrated expertise in machine learning and data-driven modeling.

Additional Information

This position is part of Lilly's postdoctoral training program, which provides an exceptional environment and the opportunity for professional growth through learning, collaboration, networking, and mentorship from Lilly's leading scientists, aimed at facilitating groundbreaking discoveries. This position is not permanent. It is for a fixed duration of two years with the potential to extend to a maximum of 4 years. You will have opportunities to apply for full-time positions after your duration is complete.

About Eli Lilly's Postdoctoral Scientist Program

When it comes to research and development, our goal is to discover and deliver innovative medicines that make life better for people around the world. It's challenging, expensive, and often filled with failure. But even when we fail, we advance medical science and understanding by learning more about diseases, biology, and chemistry—ultimately bringing new solutions one step closer to reality. Over the course of our history, we have shed light on some of the toughest health care problems known to humankind—diabetes, heart disease, infectious diseases, neuroscience disorders, cancer, and more. We could not pursue this without our research and development team. Postdoctoral scientists help us continue this pursuit. During your experience you will get:

• Top industry research experience
• Mentoring by some of Lilly's top scientists
• Laboratory and classroom training and education to further your development
• Collaboration and networking across dozens of postdoctoral scientists and other researchers

Physical Demands

The physical demands of this job are consistent with a lab environment. The physical demands here are representative of those that must be met by an employee to successfully perform the essential functions of this job.

Work Environment

This position's work environment is in a laboratory. The work environment characteristics described here are representative of those an employee encounters while performing the essential functions of this job.