Scientific Engineering Associate

Lawrence Berkeley National Lab’s (LBNL) Accelerator Technology & Applied Physics (ATAP) Division has an opening for a Scientific Engineering Associate to join the team work within the Berkeley Accelerator Controls and Instrumentation (BACI) as a vital member of a team consisting of physicists, engineers, post-doctoral scholars and students.

In this exciting role, you will be developing artificial intelligence (AI) / machine learning (ML) models for physics applications, including superconducting quantum information processors, lasers, and particle accelerators. The candidate will be implementing the ML models on field programmable gate array (FPGA), and contributing to other areas within the BACI program.

What You Will Do:

• Design and train the ML models to control and feedback complex systems, such as superconducting quantum information processors, lasers, particle accelerators. 
• Develop efficient on-line learning algorithms for updating the AI/ML model parameters in real- time. 
• Collaborate with the quantum and laser control team to implement the algorithms on field programmable gate array (FPGA) gateware, firmware and associated software for control, interlock and feedback tasks. 
• Calibrate superconducting quantum bits (qubit), perform quantum experiments to test AI/ML models. 
• Capture the measurement data on superconducting qubits/laser systems, post-processing this information, and maintain the experimental database. 
• Maintain Experimental Physics and Industrial Control System (EPICS) based device driver, runtime database and human interface for diagnostics and operations in the fiber laser lab. 
• Design and test basic multilayer printed-circuit boards. 
• Participate in daily experiment and operation of the fiber laser laboratory, in coordination of the project schedule, and the control and instrumentation needs from scientists and engineers. 
• Document system requirement specification, design approach, and testing procedures that apply to the control system to be deployed. 
• Scientific journal paper writing; give oral and/or poster presentations at conferences. 
• Participate in group and team meeting discussions. 
• Maintenance of lab notebook and detailed instrumentation logs. 
• Follow EH&S guidelines to ensure the safety of self and others in the lab and office spaces.

What is Required:

• Knowledge equivalent to Bachelor’s degree in computer science, electronic engineering, physics or related fields. Extensive related professional technical experience preferred. 
• Capable of designing, implementing, and deploying machine learning (ML) solutions. Experience with programming languages such as Python, as well as ML libraries/frameworks (e.g., TensorFlow, PyTorch). 
• Proficiency in field-programmable gate array (FPGA) programming in RTL design and verification.
• Strong background in ML, statistics, and mathematics, with practical experience in developing and deploying machine learning solutions. 
• Knowledge of deep learning techniques and frameworks (e.g., CNN, RNN, RL). 
• Strong coding skills such as Python, and familiarity with software engineering best practices. 
• Ability to work in a diverse team and interact effectively with colleagues. 
• Excellent analytical and interpersonal skills. 
• Excellent written and verbal communication skills. 
• Ability to effectively document, present, and summarize results of research and development activities. 
• Ability to learn new skills and techniques as required by the situation.

Desired Qualifications:

• Research experience, such as co-authorship in peer-reviewed publications.
• Experience in superconducting quantum bits control systems for quantum control.

Notes:

• This is a full-time 1 year, term appointment with the possibility of extension or conversion to Career appointment based upon satisfactory job performance, continuing availability of funds and ongoing operational needs.  
• The full salary range of this position is between $6,561 to $11,076 per month and is expected to pay between a targeted range of $7,385 to $9,025 per month. Salary for this position will be commensurate with the final candidate’s qualification and experience, including skills, knowledge, relevant education, certifications, and aligned with the internal peer group. It is not typical for an individual to be offered a salary at or near the top of the range for a position.
• This position may be subject to a background check. Any convictions will be evaluated to determine if they directly relate to the responsibilities and requirements of the position. Having a conviction history will not automatically disqualify an applicant from being considered for employment.
• This position is eligible for a hybrid work schedule - a combination of teleworking and performing work on site at Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA. Work schedules are dependent on business needs. Individuals working a hybrid schedule must reside within 150 miles of Berkeley Lab.

Learn About Us:

Berkeley Lab (LBNL) addresses the world’s most urgent scientific challenges by advancing sustainable energy, protecting human health, creating new materials, and revealing the origin and fate of the universe. Founded in 1931, Berkeley Lab’s scientific expertise has been recognized with 16 Nobel prizes. The University of California manages Berkeley Lab for the U.S. Department of Energy’s Office of Science.

Working at Berkeley Lab has many rewards including a competitive compensation program, excellent health and welfare programs, a retirement program that is second to none, and outstanding development opportunities. To view information about the many rewards that are offered at Berkeley Lab- Click Here.

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Berkeley Lab is an Equal Opportunity and Affirmative Action Employer. We heartily welcome applications from women, minorities, veterans, and all who would contribute to the Lab’s mission of leading scientific discovery, inclusion, and professionalism. In support of our diverse global community, all qualified applicants will be considered for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, or protected veteran status. 

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