Postdoctoral research in computational methods and quantum dynamics University of California, Merced in United States

Multiple full-time postdoctoral research positions are available in the groups of Professor Christine Isborn (Chemistry), Professor Harish Bhat (Applied Mathematics), and Dr. Henrik R. Larsson (Chemistry and Physics) at the University of California Merced.

The successful candidates will join a DOE-funded SciDAC project titled 'Moving Electrons through Space and Time: Enabling the Quantum Dynamics of Chirality-Induced Spin Selection Through Novel and Scalable Computational Methods.' This interdisciplinary project involves close collaboration with teams at the University of Michigan, Rutgers University Newark, and Lawrence Livermore National Laboratory, and offers access to advanced computational resources including shared clusters at Merced, dedicated group nodes, and the NERSC supercomputing cluster. The Isborn group focuses on applying time-dependent electronic structure and nonadiabatic dynamic methods to model the CISS effect in molecular systems, with potential method development in time-dependent current DFT. The Bhat group is developing new mathematical and computational methods at the intersection of scientific computing and machine learning, aiming to build neural network models of potentials in Hamiltonians for time-dependent quantum systems. The Larsson group is advancing methods based on the density matrix renormalization group (DMRG) for real-time electron dynamics, with applications to chiral molecules and density functional inversion, as well as quasi-exact nonadiabatic nuclear dynamics simulations using tensor network states (ML-MCTDH).

Postdoctoral scholars will be expected to present at national and international conferences, publish journal articles, and mentor students. Applicants must hold a Ph.D. in Chemistry, Physics, Applied Mathematics, Mathematics, Control, Operations Research, Statistics, or a related field. Required skills include quantum simulations, numerical analysis, scientific computing, optimization or machine learning, and programming (Python, Julia, C++, Fortran, NumPy/SciPy, JAX/PyTorch/TensorFlow). Excellent oral and written communication skills are essential.

Ideal candidates will have experience in developing computational methods in theoretical chemistry/physics or machine learning and PDE-constrained optimization for time-dependent systems. The position is for two years, with a salary range of $69,073 - $82,836. Candidates must be eligible to work in the US, as visa sponsorship is not available. The application requires a CV, cover letter, Authorization to Release Information Form, and contact information for three references.

Optional materials include examples or links to programming code. The University of California Merced is an R1 institution committed to innovation and excellence in research, with a diverse and inclusive academic environment.