Postdoctoral Fellow – AI/ML Enabled Bioprocess Modeling and Control

Tasks

• Apply machine learning and deep learning for phenotypic clustering and performance prediction
• Collaborate with cross functional teams and mentor junior scientists
• Communicate results through presentations technical discussions and publications
• Design and execute shake flask and bioreactor experiments to generate process and omics datasets
• Develop hybrid mechanistic data driven models for mammalian cell culture processes
• Develop model predictive control frameworks for real time monitoring and control
• Explore agentic AI frameworks for model fitting validation and transfer learning
• Extend mechanistic bioprocess models with additional physiological functions using kinetic genome scale or data driven modeling
• Integrate transcriptomic and omics data for clone performance and stability prediction
• Maintain documentation in electronic laboratory notebooks and technical reports
• Perform in silico sensitivity and scenario analyses for robustness and control drivers
• Validate models and control strategies using historical and new datasets and deploy prospectively

Perks/Benefits

• 401k matching
• Dental coverage
• Health insurance
• Medical coverage
• Medical leave
• Paid Holidays
• Paid caregiver/parental leave
• Paid vacation
• Prescription Drug Coverage
• Relocation support
• Retirement savings contribution
• Vision coverage

Skills/Tech-stack

Classification | Clustering | Control Systems | Data-Driven Modeling | Data-driven | Deep learning | Genome-scale modeling | Hybrid Modeling | Julia | Kinetic modeling | MATLAB | Machine Learning | Mechanistic modeling | Model Validation | Model-Predictive Control | Optimization | Predictive control | Python | Regression | Scale modeling | Scenario Analysis | Sensitivity Analysis | Transcriptomics | Transfer Learning | ‘Omics