Career-Track Research Scientist

Lawrence Berkeley National Laboratory • Berkeley, CA

1d • $93,504 - $224,400 • Onsite

About The Position

The Applied Energy Materials group within the Energy Technologies & Systems Division at Lawrence Berkeley National Laboratory (Berkeley Lab) studies materials for electrochemical energy storage and conversion, combining experimental and computational research. Computational research focuses on discovering and understanding energy materials and molecules, spanning first-principles simulation, machine learning, and automated high-throughput workflows deployed on DOE supercomputers.
The Division is seeking a Career-Track Chemist Research Scientist to conduct creative computational research on molecular reaction kinetics in energy technologies, collaborate with scientific staff, and contribute to funded projects. The scientist will develop and apply methods for predicting reaction pathways and reaction rates for molecular systems at scale, with applications including electrolyte stability and degradation in batteries. The work combines quantum chemistry and machine-learning models within automated open-source workflows. The position starts within an established research program and is expected to grow into an independent research direction by mid-term of the appointment.
This position has an anticipated start date of November 2, 2022.
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

• Advanced degree in chemistry, physics, materials science, or a related field, and 3-5 years of relevant professional experience (includes graduate research)
• Demonstrated experience applying advanced principles, theories, and concepts to R&D problems in computational chemistry or computational materials science
• A publication record in computational chemistry, reaction kinetics, or a closely related area
• Excellent academic record and evaluations
• Experience applying for and using high-performance computing resources
• Experience with collaborative software development in Python via GitHub
• Experience with high-throughput simulation and computational workflow frameworks
• Must be a U.S. Citizen
• Candidates must be eligible to work in the U.S. at the time of hire. Visa sponsorship is not available for this position.

Nice To Haves

• Ph.D. in chemistry, physics, materials science, or a related field
• Postdoctoral or equivalent experience
• Experience contributing to funding proposals
• Experience mentoring students or junior researchers
• Experience managing a team of researchers or working within large scientific collaborations
• Experience with chemical reaction networks, graph algorithms, or pathfinding
• Experience with reaction kinetics, transition-state finding, or kinetic Monte Carlo
• Proven record of publications and achievements
• Ability to conduct creative research within an established research program
• Proficiency in Python
• Expertise in molecular computational chemistry and density functional theory
• General knowledge of electrochemistry and energy storage materials
• Ability to collaborate effectively with a multidisciplinary team of scientists and external collaborators
• Excellent written, verbal, and presentation skills
• Ability to work effectively in a team environment as well as independently
• Ability to manage competing deadlines across multiple projects
• Machine learning for molecular or materials property prediction
• Software engineering practice: version control, unit testing, continuous integration

Responsibilities

• Conduct research on reaction kinetics and reaction networks within an established research framework, developing toward independent research by mid-term of the appointment
• Develop and deploy open-source high-throughput computational workflows for molecular and materials simulation
• Build machine-learning models for reaction and molecular property prediction
• Collaborate with Lab scientific staff on projects requiring intellectual leadership and creativity
• Contribute to or co-author publications in peer-reviewed journals, and lead-author publications by mid-term
• Contribute to the preparation of funding proposals
• Present findings at seminars and conferences
• Apply for computational time on DOE supercomputing resources
• Build a network of contacts and collaborators within the field and at funding agencies
• Report research progress to funders
• Provide scientific direction to students and junior researchers
• Apply expertise in computational chemistry with a working grasp of related disciplines (electrochemistry, materials science, machine learning)
• Act as liaison between PIs/scientists, other LBNL employees, and external collaborators

Benefits

• Exceptional health and retirement benefits, including pension or 401K-style plans
• A culture where you’ll belong - we are invested in our teams!
• In addition to accruing vacation and sick time, we also have a Winter Holiday Shutdown every year.
• Parental bonding leave (for both mothers and fathers)
• Pet insurance

What This Job Offers

• Job Type: Full-time
• Career Level: Senior