Position overviewPosition title: Postdoctoral Scholar
Open date: August 24, 2023
Next review date: Wednesday, Nov 1, 2023 at 11:59pm (Pacific Time)
Apply by this date to ensure full consideration by the committee.
Final date: Friday, Dec 15, 2023 at 11:59pm (Pacific Time)
Applications will continue to be accepted until this date, but those received after the review date will only be considered if the position has not yet been filled.
Prof. Harish S. Bhat (http://faculty.ucmerced.edu/hbhat/) in Applied Mathematics at UC Merced is seeking applicants for a postdoctoral position focused on automated learning of reduced-order models for quantum dynamics. The postdoctoral scholar will work on new mathematical and computational methods to learn tractable models that accurately predict the dynamics of time-dependent quantum systems. Systems/problems of interest include electron dynamics, nuclear/spin dynamics, and optimizing coherence times for quantum circuits.
The postdoctoral scholar will contribute to methods that fuse first-principle modeling (including numerical analysis and scientific computing) with machine learning modeling of quantum Hamiltonian terms (e.g., using neural networks). To train these models, the postdoctoral scholar will develop and apply optimization methods that are constrained by time-dependent, physical dynamics with symmetries and invariants. The postdoctoral scholar will be free to develop and incorporate ideas from a diverse array of subfields including physics-constrained learning, equation discovery, dimensionality reduction, interpretable machine learning, geometric mechanics, and/or optimal control. The resulting methods will enable simulation and control of quantum systems that cannot be handled by currently available methods.
The postdoctoral scholar will work closely with Prof. Bhat and will also be co-mentored by Prof. Christine Isborn (Chemistry, UC Merced). The postdoctoral scholar will be expected to publish in peer-reviewed journals and proceedings, to develop and publish open-source software, to present research findings at conferences, and to work with graduate and undergraduate student researchers.
A PhD in Applied Mathematics, Control & Dynamical Systems, Theoretical/Computational Physics, Theoretical/Computational Chemistry, or a related field
Experience developing new computational methods and implementing these methods in code, e.g., with Python and NumPy/SciPy
Interest in machine learning and quantum dynamics
Ability to effectively communicate verbally and in writing
Experience publishing peer reviewed articles and presenting at technical conferences
Experience with one or more of the following areas: PDE-constrained optimization, optimal control, numerical simulation of quantum systems, physics-constrained learning, and/or geometric mechanics
Proficiency in scientific computing on modern clusters with GPU nodes
Experience with machine learning frameworks such as JAX, PyTorch, and/or TensorFlow
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