Internship: Mixed-Integer Optimal Control Algorithms
DescriptionMERL is looking for highly motivated individuals to work on efficient numerical algorithms and applications of mixed-integer optimal control methods. The research will involve some among the following: the study and development of mixed-integer optimization techniques for optimal control, the implementation and validation of algorithms for relevant control applications. The ideal candidate should have experience in Newton-type optimization algorithms, techniques for mixed-integer optimization and/or model predictive control. PhD students in engineering or mathematics with a focus on nonconvex, mixed-integer optimization or numerical optimal control are encouraged to apply. Publication of relevant results in conference proceedings and journals is expected. Capability of implementing the designs and algorithms in Matlab is expected; coding parts of the algorithms in C/C++ is a plus. The expected duration of the internship is 3-6 months and the start date is flexible.
Research Area: Mechatronics
Contact: Rien Quirynen
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