The University of California, Merced seeks a postdoctoral researcher in machine learning under the direction of Prof. Harish S. Bhat (Applied Mathematics) and Prof. Christine Isborn (Chemistry).
The researcher will work on a project that seeks to improve simulations of charge transfer in a variety of molecules and materials. The project involves: (i) developing models (such as RNNs) 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 computer science, statistics, applied mathematics, 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 (RNNs), autoencoders, probabilistic models, equation discovery, reduced-order modeling and/or system identification; experience with large spatiotemporal data sets; proficiency with TensorFlow or similar frameworks. 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 ([email protected]) for more information. Best Regards, Harish p.s.: If you happen to be at ECML this week in Würzburg and are interested in this position, please let me know so I can describe the project to you in person! _______________________________________________ uai mailing list [email protected] https://secure.engr.oregonstate.edu/mailman/listinfo/uai
