We are looking for a postdoctoral researcher with (a) expertise in classical computational methods for non-linear mixed integer linear programming, and (b) knowledge of quantum computing (based in Ising solvers and QUBO, Quadratic Unconstrained Binary Optimization).
Project Description: Due to limited qubit handling capacity of current quantum systems, a major focus would be on the development of decomposition approaches that are natively implementable on quantum and quantum-inspired systems. The research aims to understand fundamental underpinnings of quantum-inspired optimization and develop innovative decomposition optimization methods for quantum and quantum-inspired optimization approaches for solving non-linear large-scale (mixed) integer programming problems (MINLP). Benchmarking such decomposition methods on the new quantum-inspired hardware being built against available quantum hardware and best-in-class classical methods is central to the project. Metrics include computing-communication trade-offs and energy use (and sustainability analyses) in addition to the quality of solutions and total time. A key focus would be on the development of hardware agnostic decomposition methods, however, there is scope for exploiting specific hardware characteristics for co-optimization. The problem scenarios include classical benchmark problems such as max-cut and Boolean satisfiability type problems, but, domain specific MINLP formulations such as those arising in energy system optimization will be considered. The research is part of several sponsored projects, including the DARPA QuICC program, thus providing the candidate opportunities to engage with academic and industry collaborators of the co-advisors.
Project Start Date: January 1, 2023 preferred but no later than 06/01/2023.
Strong mathematical, programming and computational skills and background in relevant areas, such as graph theory, optimization, control, signal processing, or machine learning. Knowledge of Quantum Computing and Quantum Complexity Theory is not a requirement but preferred, but the ability to learn new methods independently is.
Passion and interest to solve challenging research problems motivated from practical applications using methodologies from different areas.
Excellent verbal communication and writing skills (English).
Ability to thrive in a collaborative environment.
Sridhar Tayur (Tepper, CMU)
Soummya Kar (ECE, CMU)
Perform and publish fundamental research on quantum integer optimization in collaboration with the co-advisors and their research groups.
Develop quarterly progress and technical reports with the co-advisors on sponsored research projects.
Encouraged to write research grant proposals with the co-advisors and colleagues at CMU.
Encouraged to co-teach relevant CMU courses.
Help in creating educational/expository materials on state-of-the-art topics related to the project.
Interested candidates should send the following material to via Interfolio: curriculum vitae, up to three publications or working papers, and three letters of recommendation. To receive full consideration, applications must be completed by December 23, 2022 or until position is filled.
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