Research Fellow in Data and Computational Science (Machine Learning and Multiobjective Optimization)

Position Overview

This position will undertake a study in the use of Machine Learning for addressing various Optimization problems. The goal is utilizing Machine Learning, in particular Deep Learning, to build generic and adaptive solvers for Multi-Objective Optimization. This position is with a one-year contract under a particular grant.

Reports to

Associate Professor of Data and Computational Science

Essential Duties

• Performing the specified research tasks in using Machine Learning for Optimization.
• Publishing at least one peer-reviewed article as the first author.
• Perform other duties as assigned.
• University employees' job responsibilities will continue to expand in scope and depth as the University grows in size and complexity in its programs.

Required Qualifications

• Master or Ph.D. in Computer Science, Artificial Intelligence, Operations Research, or another related discipline.
• Machine Learning background and expertise in the well-known Machine Learning / Deep Learning libraries of Python such as TensorFlow and PyTorch.
• Knowledge in basic problem modelling and algorithm design for Combinatorial Optimization and Multi-objective Optimization.
• Effective communication and writing skills in English.

Education

Master or Ph.D. in Computer Science, Artificial Intelligence, Operations Research, or another related discipline.

Years of Related Working Experience

Prior working experience is preferable yet not required.