Computational Postdoctoral Fellow – Quantitative Modeling

About the role

The Computational Postdoctoral Fellow will join the Quantitative Modeling Group at Berkeley Lab’s Biological Systems and Engineering Division. The role focuses on applying AI, data science, mechanistic modeling, robotics, and synthetic biology to create predictive models of biological systems and support self‑driving laboratory initiatives.

Key responsibilities

• Integrate and analyze multi‑omics datasets (fluxomics, transcriptomics, proteomics, metabolomics).
• Develop quantitative computational models that predict outcomes of bioengineering interventions.
• Apply Monte Carlo sampling to quantify model uncertainty.
• Design and implement machine‑learning algorithms and combine them with mechanistic models.
• Collaborate with bench scientists, automation engineers, and software developers to create high‑throughput data pipelines.
• Contribute to the development of self‑driving laboratory workflows.

Required profile

• Ph.D. in a quantitative discipline such as computational biology, bioengineering, systems biology, or related field.
• Experience working with large biological datasets and building predictive models.
• Strong publication record demonstrating interdisciplinary research.

Required skills

• Artificial Intelligence
• Data science
• Mechanistic modeling
• Robotics
• Synthetic biology
• Multi‑omics data integration
• Monte Carlo sampling
• Machine learning
• Data mining
• Algorithm development
• Laboratory automation

What we offer

• Exceptional health benefits.
• Generous paid time off, sick leave, and holidays.
• An inclusive culture that supports professional growth.