Research Fellow (Mathematics)
Interested applicants are invited to apply directly at the NUS Career Portal. Please note your application will only be processed if you apply via NUS Career Portal.
NUS Career Portal link: https://careers.nus.edu.sg/job/Research-Fellow-%28Mathematics%29/34033-en_GB/?st=C2103F1667BB5803ED55E6BC04FF6C7282F700F9
We regret that only shortlisted candidates will be notified.
Job Description
The successful candidate will work with Professor Toh Kim Chuan on methods for structured nonsmooth nonconvex optimization under a project on "Methods for nonsmooth nonconvex optimization".
The main responsibilities of the position include the following:
• Conduct independent research on the design, analysis and implementation of efficient and robust algorithms for large-scale structured nonconvex optimization problems.
• Conduct simulations and experiments to validate established theoretical results and evaluate the performance of the proposed algorithms.
• Prepare technical reports, research papers, and presentations for academic conferences and journals.
• Work closely with faculty members, postdocs, and PhD students on research projects.
Qualifications / Discipline:
• Graduating PhD or recent PhD holder specializing in computational optimization.
Skills:
• Advanced knowledge on optimization theory and algorithms at PhD level is required, especially strong theoretical and numerical understanding of advanced nonsmooth and/or nonconvex optimization algorithms for matrix and discrete optimizations.
• Proficiency in Python or Julia, Matlab, C++ for algorithmic implementation.
• Good academic writing and presentation skills for publishing research findings.
• Ability to work independently and collaboratively in an interdisciplinary research environment.
Experience:
• At least 2 years of independent research experience in the design and analysis of efficient algorithms for solving large-scale nonconvex nonsmooth optimization problems.
• Demonstrated research and intellectual ability, such as having published research papers in premier optimization journals related to the job requirements.