Postdoctoral Fellow – AI/ML Enabled Bioprocess Modeling and Control

Pfizer • Andover, MA

1d • Onsite

About The Position

The Upstream Process Development group within the Bioprocess R&D organization is seeking a Postdoctoral Fellow – AI/ML Enabled Bioprocess Modeling and Control. The successful applicant will join a team of scientists and engineers focused on developing and optimizing manufacturing processes for recombinant proteins and other modalities for early- and late-phase human clinical trials. This role will focus on developing and applying innovative mathematical and computational modeling approaches to characterize, understand, and predict complex biological systems used for recombinant protein vaccine and therapeutic production. The postdoctoral fellow will develop hybrid mechanistic and data‑driven models for mammalian cell culture processes and leverage transcriptomic and other omics data to enable early clone selection based on predicted process performance and generational stability. In addition, the individual will develop a model predictive control (MPC) framework that uses these models to enable real‑time monitoring and control of cell culture processes. Further, the postdoctoral fellow will design and conduct targeted experiments to generate data for model development, training, validation, and control strategy evaluation. The role will also explore agentic AI approaches to orchestrate model fitting, transfer learning, and deployment across portfolio projects, enabling scalable and adaptive reuse of models for early decision‑making and process control. This position is well suited for a highly motivated scientist with strong expertise in machine learning, systems biology, kinetic modeling, process control, and mammalian cell metabolism.

Requirements

• PhD in Chemical Engineering, Biochemical Engineering, Bioengineering, Systems Biology, Computational Biology, or a closely related field (0–2 years postdoctoral experience).
• Less than 2 years of post-degree (PhD) experience.
• Willingness to make a minimum 2-year commitment.
• Successful record of scientific accomplishments evidenced by scientific publications and/or presentations with at least one first-author publication in a peer-reviewed journal.
• Two letters of recommendation are also required prior to interview stage.
• Strong foundation in mathematical modeling, chemical/biochemical reaction kinetics, and mammalian cell culture processes.
• Proficiency in scientific computing using Python, Julia and/or MATLAB (experience with control toolboxes, optimization solvers, or ML libraries is a plus).
• Strong analytical, problem‑solving, and communication skills, with the ability to work independently and in interdisciplinary teams.
• Demonstrated expertise in machine learning and data‑driven modeling, including regression, classification, clustering, and model validation.
• Experience integrating omics data (especially transcriptomics) with mechanistic or hybrid models.
• Permanent work authorization in the United States.

Nice To Haves

• Familiarity with process systems engineering and control concepts, including model predictive control, optimal control, or dynamic optimization.
• Experience building modular, reusable AI/ML workflows that support transfer or multi‑task learning across related systems, with familiarity in adaptive decision‑making and human‑in‑the‑loop modeling concepts.
• Understanding of CHO cell physiology, central carbon and amino acid metabolism, and regulatory mechanisms relevant to bioprocessing.
• Hands‑on experience designing and executing cell culture experiments is highly desirable.

Responsibilities

• Develop hybrid mechanistic–data driven models for mammalian cell culture processes supporting recombinant protein production.
• Integrate transcriptomic and other omics data as structured inputs for clone specific performance and stability prediction.
• Apply machine learning and deep learning methods for phenotypic clustering, parameter estimation, and performance prediction.
• Extend existing mechanistic bioprocess models to include additional physiological functions (e.g., amino acid metabolism, regulatory feedback loops) using kinetic, genome scale, or data driven modeling approaches.
• Design and implement model predictive control (MPC) frameworks using mechanistic and hybrid models for real time control of critical process variables (e.g., feeding strategies, metabolite control).
• Design and execute shake flask, ambr®, or bench scale bioreactor experiments to generate process and omics datasets for model development and validation.
• Perform in silico sensitivity and scenario analyses to understand process robustness, control leverage, and drivers of performance and stability.
• Validate models and control strategies using historical and new datasets, and deploy them prospectively to support new development programs.
• Explore agentic AI frameworks to orchestrate model fitting, validation, and transfer learning across portfolio projects, enabling scalable adaptation of models to new clones and processes with human‑in‑the‑loop decision support.
• Maintain rigorous documentation in electronic laboratory notebooks and internal technical reports.
• Communicate results effectively through presentations, technical discussions, and peer reviewed publications.
• Collaborate with cross functional teams across different time zones and contribute to mentoring junior scientists as appropriate.

Benefits

• 401(k) plan with Pfizer Matching Contributions and an additional Pfizer Retirement Savings Contribution
• paid vacation, holiday and personal days
• paid caregiver/parental and medical leave
• health benefits to include medical, prescription drug, dental and vision coverage
• Relocation support available

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree