Many data modeling problems can be formulated as standard optimization problems in which a scalar cost function is minimized with respective to some parameters in system design. However conventional optimization procedures are often insufficient for practical problems since often more than one objective function need to be optimized simultaneously. Multi-objective optimization has been applied in many scientific and engineering applications. They are particular useful for decision support in exploring different system designs. A PhD project on multi-objective optimization can include many interesting problems. For example its optimization algorithmic design is inherently not straightforward. It is often hybrid with evolutionary algorithms, mixed-integer problems, etc. A central concept in multi-objective optimization is Pareto optimality representing a set of non-dominated optimal solutions. The modeling of Pareto front may be integrated in the optimization procedure as interactive optimization algorithms. The high-order multi-objective optimization is also open to research. These researches will provide solid footing for highly demanding data scientist jobs. Applicants with applied mathematics and/or electrical engineering degrees are particularly welcome.