Computational Biologist Research Scientist (Quantitative Modeling Group)

Lawrence Berkeley National Laboratory•Berkeley, CA

1d•$110,484 - $132,588•Onsite

About The Position

Berkeley Lab’s (LBNL) Biological Systems and Engineering (BSE) Division has an opening for a Computational Biologist Research Scientist to join the Quantitative Modeling Group led by Héctor García Martín! In this exciting role, you will work towards making biology predictable by integrating artificial intelligence (AI), robotics, and synthetic biology. Under general supervision of the Principal Investigator (PI), you will leverage current and future funding to build and leverage tools and infrastructure that advance biomanufacturing through machine learning and automation. You will be expected to secure independent funding and develop a vigorous research program focused on the application of AI to advance scientific discovery and innovation. This position has an anticipated start date of December 1, 2026. We’re here for the same mission, to bring science solutions to the world. Join our team and YOU will play a supporting role in our goal to address global challenges! Have a high level of impact and work for an organization associated with 17 Nobel Prizes!

Requirements

A 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 minimum of 3 years of relevant experience applying artificial intelligence to guide and inform biological experiments or an equivalent combination of education and experience.
Significant experience with Python or other major programming languages.
Experience with deep learning techniques.
Experience with lab automation and automated experimental workflows.
Experience with experimental bench work.
A strong mathematical background including the ability to apply quantitative approaches to complex scientific problems.
Strong analytical skills including experience identifying issues, troubleshooting complex problems, and recommending appropriate solutions.
Excellent written and verbal communication skills including the ability to effectively communicate scientific concepts and research findings to a variety of audiences.
Demonstrated interpersonal skills including the ability to conduct collaborative research in an interdisciplinary team environment.

Nice To Haves

A 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 effectively supervise staff, including assigning work, providing coaching and performance feedback, and supporting employee development.

Responsibilities

Apply artificial intelligence and data-mining approaches to guide and advance biomanufacturing.
Develop quantitatively 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 future research directions and experimental approaches.
Design and leverage data management systems to effectively organize, represent, and utilize biological data.
Collaborate continuously with experimentalists to guide and propose new experimental work, and use available data to its full potential.
Develop, integrate, and utilize robotic systems to automate scientific workflows and advance the development of self-driving labs.
Prepare research results for publication in scientific journals and presentation at scientific conferences, seminars, and internal meetings.
Assist with the preparation and submission of grant proposals and other funding applications.
May be responsible for their team's success and professional development. This may involve recruiting talent with the right skills for their projects, directing daily work, and managing performance through feedback and clear expectations. Supervisors have the authority to take necessary actions, including performance management, to maintain the research group's integrity and productivity.