Organisation Name: Lawrence Berkeley National Laboratory
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Job Description:
The Scalable Solvers Group is hiring for a Computational Chemistry Research Scientist to join the Computational Research Division (CRD) at Lawrence Berkeley National Laboratory (LBNL) to engage in research on the development of efficient linear and nonlinear solvers with applications to computational chemistry problems of interest to the U.S. Department of Energy (DOE). What You Will Do: Contribute to algorithmic design and code development in several areas of fast solver for computational chemistry. In particular, develop efficient symmetric eigensolvers for Hartree-Fock, density functional theory, configuration interaction and other high-level methods, nonlinear solvers for coupled cluster models and model order reduction methods for analyzing time-dependent quantum many-body problems. Be familiar with standard quantum chemistry software tools such as NWChem, Gaussian as well as emerging software tools such DGDFT, and implement new algorithms in these software packages. Highly skilled in high performance computing, and especially with experience in programming for GPU accelerated computing platforms. Participate in a multidisciplinary team involving mathematicians, computer scientists, and domain scientists for developing and deploying eigenvalue computation techniques in the solution of DOE science problems. What is Required: PhD degree or equivalent experience in applied mathematics or computation science. Demonstrated experience in solving large-scale linear and nonlinear problems in computational chemistry. Demonstrated experience with model order reduction techniques for analyzing time-dependent quantum many-body problems Demonstrated experience in programming massively parallel computer platforms, especially GPU accelerated systems. Demonstrated proficiencies in more than one computer programming language. In-depth knowledge of complexity and performance analysis and advanced data structures. Knowledge of quantum many-body problems and Hartree-Fock, density functional theory and coupled cluster models. Knowledge of sparse matrix techniques. Knowledge of MPI, OpenMP, CUDA and other parallel programming models is crucial. Post-doctoral experience is highly preferred. The posting shall remain open until the position is filled. Notes: This is a full-time, 5 year, career-track term appointment that may be converted to career based upon satisfactory job performance, continuing availability of funds, and ongoing operational needs. 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. Work will be primarily performed at Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA.
