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Job Description
The successful candidate will work with Assoc Prof Chen Ying on Quantum Computing and Optimization Algorithms under a project on "Computer Science Approaches to Quantum Computing for Finance".
The successful candidate will focus on advancing quantum-ready algorithms, specifically in the areas of QUBO (Quadratic Unconstrained Binary Optimization) and Integer Linear Programming (ILP). The role involves developing, optimizing, and benchmarking these algorithms on various platforms, including classical and quantum computing environments.
Key Responsibilities:
- Develop and optimize QUBO and ILP formulations for complex optimization problems.
- Benchmark various solvers, including but not limited to GUROBI, SCIP, and quantum solvers.
- Collaborate with cross-disciplinary teams to implement and document challenging problems.
- Mentor and guide undergraduate students in their final projects and research endeavors.
- Present research findings at conferences and contribute to high-impact publications.
Qualifications / Discipline:
- A Bachelor’s degree in Mathematics, Computer Science, or a related field, with a focus on optimization algorithms or quantum computing.
- Proven experience in developing and optimizing QUBO and ILP formulations.
Skills:
- Strong programming skills in languages such as Python, Rust, and Java.
- Solid understanding of numerical mathematics and discrete algorithms.
- Experience in benchmarking solvers and working with quantum computing platforms is highly desirable.
Experience:
- Experience with quantum many-body physics or machine learning algorithms.
- Previous experience in a research or academic setting, particularly in guiding students.
