Computational Research Scientist (Quantitative Modeling Group)

Lawrence Berkeley National Laboratory•Berkeley, CA

2d•$94,740 - $151,580•Onsite

About The Position

Berkeley Lab’s Biological Systems and Engineering Division is hiring a Computational Research Scientist to join the Quantitative Modeling Group, led by Héctor García Martín. Under general supervision of the Principal Investigator, the scientist will use current and future funding to build and implement tools, technologies, and infrastructure that advance biomanufacturing through machine learning and automation. The position focuses on integrating artificial intelligence, robotics, and synthetic biology to make biology more predictable and advance scientific discovery and innovation. The successful candidate is expected to secure independent funding and develop a vigorous research program applying AI to scientific discovery. This is a full-time, benefits-eligible, career-track term appointment for two years. It may be renewed for up to five years and may be converted to a career appointment, based on satisfactory performance, continued funding, and operational needs. The role is represented by a union and is exempt from overtime pay. Work is performed onsite at Berkeley Lab’s Emeryville Station East Operations Center in Emeryville, California. The anticipated start date is December 14, 2026.

Requirements

Ph.D. or equivalent knowledge/training in Systems Biology, Bioengineering, Computational Biology, Bioinformatics, Applied Mathematics, Physics, Computer Science, Chemical Engineering, Electrical Engineering, or a closely related discipline, and at least three years of relevant experience applying artificial intelligence to guide and inform biological experiments; an equivalent combination of education and experience is acceptable.
Significant experience with Python or another major programming language.
Experience with deep learning techniques and methodologies.
Experience with lab automation and automated experimental workflows.
Experience with experimental synthetic biology and hands-on bench work.
Strong mathematical background and ability to apply quantitative approaches to complex scientific problems.
Strong analytical skills, including identifying issues, troubleshooting complex problems, and recommending appropriate solutions.
Excellent written and verbal communication skills, including the ability to communicate scientific concepts and research findings to varied audiences.
Interpersonal skills and ability to conduct collaborative research in an interdisciplinary team environment.

Nice To Haves

Ph.D. in Systems Biology, Bioengineering, Computational Biology, Bioinformatics, Applied Mathematics, Physics, Computer Science, Chemical Engineering, Electrical Engineering, or a closely related discipline.
Demonstrated ability to supervise staff effectively, including assigning work, providing coaching and performance feedback, and supporting employee development.

Responsibilities

Apply artificial intelligence and data science to guide and advance biomanufacturing.
Develop quantitative and predictive models of cell metabolism.
Integrate transcriptomic, proteomic, and metabolomic data into quantitative predictive models of biological systems.
Develop and orchestrate AI agents to analyze complex scientific data and recommend research directions and experimental approaches.
Design and use data management systems to organize, represent, and utilize biological data effectively.
Collaborate with experimentalists to identify research opportunities, propose and guide experiments, and make effective use of available data.
Develop, integrate, and use robotic systems to automate scientific workflows and advance self-driving labs.
Prepare research results for publication in scientific journals and presentation at conferences, seminars, and internal meetings.
Assist with preparing and submitting grant proposals and other funding applications.
May be responsible for team success and professional development.
Secure independent funding and develop a vigorous research program focused on applying AI to scientific discovery and innovation.
May supervise staff, including assigning work, coaching, providing performance feedback, and supporting employee development.