The University of California, Merced seeks a postdoctoral researcher under the direction of Prof. Harish S. Bhat (Applied Mathematics) and Prof. Christine Isborn (Chemistry).
The researcher will develop and apply machine learning methods to improve simulations of charge transfer in a variety of molecules and materials. The project involves: (i) developing models (such as recurrent neural networks) to predict excited-state electron dynamics, (ii) learning interpretable potential energy terms for use in time-dependent density functional theory (TDDFT), and (iii) learning optimal projection operators for nonadiabatic dynamics.
We seek applicants with a Ph.D. in statistics, applied mathematics, computer science, or closely related field. We are particularly interested in applicants with the following qualities: excellent written and spoken communication skills; expertise in machine learning including recurrent neural networks, autoencoders, probabilistic models, equation discovery, reduced-order modeling and/or system identification; experience with large spatiotemporal data sets; proficiency with TensorFlow or PyTorch.
This position is funded by a recent US Department of Energy grant; the official UC Merced job link will be online soon. Please contact me directly (
hbhat@ucmerced.edu) for more information.
Best Regards,
Harish
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Harish S. Bhat
Associate Professor
University of California, Merced
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