Research Fellow (Mixed Integer Programming)

National University of Singapore; Singapore; On-site; SGD 60,000 - 90,000; Full time

Job summary

The National University of Singapore invites applications for a postdoctoral position in optimization and related areas. The successful candidate will work under Dr.

Guanyi Wang in ISEM to develop cutting-edge algorithms, prove rigorous guarantees, and implement numerical simulations. Key research areas include MILP/Quadratic Programming for large-scale problems, non-convex optimization, and structured learning.

Qualifications

• PhD in Optimization, Operations Research or related fields.
• Solid research background in Mixed-Integer Programming, Nonlinear Optimization, and/or Structured Learning Theory.
• Strong publication record in top-tier optimization journals preferred.

Responsibilities

• Work on Mixed-Integer Linear/Quadratic Programming for large-scale decision-making problems.
• Investigate non-convex optimization problems and provide theoretical analysis.
• Explore structured learning and its applications in optimization and related areas.

Skills

Mixed-Integer Programming

Nonlinear Optimization

Structured Learning

Education

PhD in Optimization/OR

Job description

Company description:

The National University of Singapore is the national research university of Singapore. Founded in 1905 as the Straits Settlements and the Federated Malay States Government Medical School, NUS is the oldest higher education institution in Singapore

Job description:

Job Description

This postdoc will work under the supervision of Dr. Guanyi Wang in ISEM at NUS to explore cutting-edge algorithms, derive rigorous theoretical guarantees, and implement numerical simulations.

The postdoc will engage in research including, but not limited to, the following areas:

• Mixed-Integer Linear / Quadratic Programming for large-scale decision-making problem
• Non-Convex Optimization and theoretical analysis
• Structured Learning & Learning Applications

Job Requirements

A qualified candidate should satisfy the following requirements:

• A Ph.D. degree in Optimization, Operations Research or related fields
• A solid research background in Mixed-Integer Programming, Nonlinear Optimization, and/or Structured Learning Theory.
• A strong publication record in top-tier optimization journals is preferred.