Computational Career-Track Research Scientist

Lawrence Berkeley National Laboratory • Berkeley, CA

3d • Hybrid

About The Position

The Scalable Solvers Group in the Applied Mathematics and Computational Research Division at Lawrence Berkeley National Laboratory (LBNL) is seeking a Computational Career-Track Research Scientist.
The successful candidate will engage in the group’s research and development (R&D) activities in high-performance computing (HPC) and contribute in setting research agendas, with a focus on sparse linear and tensor algebra, randomized algorithms and quantum algorithms. Additionally, the role involves applying these algorithms to modeling and simulation of physical systems, incorporating uncertainty quantification and AI/ML techniques for scientific disciplines.

Requirements

• PhD or equivalent experience in applied mathematics, computer science, or a related field
• Demonstrated experience in developing algebraic solvers
• Proficiency in more than one computer programming language, such as Python, C/C++, CUDA, Fortran
• Knowledge of complexity and performance analysis and advanced data structures
• Knowledge of sparse matrix techniques
• Knowledge of methods for solving linear systems and eigenvalue problems
• Experience of MPI, OpenMP, and other parallel programming models
• Understanding of quantum many-body problem and tensor eigenvalue problem
• Excellent oral and written communication skills
• Ability to conduct independent research
• Ability to collaborate effectively with a multidisciplinary research team

Nice To Haves

• Postdoctoral experience in a relevant field
• Experience in programming massively parallel computer platforms

Responsibilities

• Contribute to the design and integration of advanced algorithms in one or more of the following areas, with opportunities to take on increasing responsibility over time: Solvers and preconditioners for sparse structured linear systems
• Solvers and preconditioners for sparse structured linear and nonlinear eigen systems
• Tensor algebraic problems
• Bayesian statistical methods, leading to robust and scalable UQ methods
• Linear algebra and tensor algorithms for quantum computing
• AI/ML algorithms for HPC code development
• Novel algorithms for advancing AI/ML methodology
• Assist with code optimization and integration into Department of Energy (DOE’s) applications running on the exascale computer systems with GPU accelerators

Benefits

• Exceptional health and retirement benefits, including pension or 401K-style plans
• Opportunities to grow in your career - check out our Tuition Assistance Program
• 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: Mid Level
• Education Level: Ph.D. or professional degree
• Number of Employees: 101-250 employees