Are you an aspiring researcher, fascinated by theoretical computer science? And would you like to conduct research at the intersection of automata learning, testing, and category theory? As a PhD candidate, you will help boost the scalability of automata learning through the development of approximation and abstraction methods in learning algorithms and the underlying theory.
You will contribute to model learning, which is a family of techniques for automatically constructing automata models of black-box systems, by systematically running tests and making observations. These techniques have been applied to analyse network protocols, legacy software, smart card readers and embedded control software. As a PhD candidate on this project, you will help to boost the scalability of automata learning through the development of approximation and abstraction methods in learning algorithms and the underlying theory. Your research will have a strong focus on developing the foundations of automata learning, using category theory and coalgebra as a formalism of choice, enabling a broad application of the results. As such, you will conduct research at the intersection of automata learning, testing and category theory.
This PhD position is part of an NWO VIDI project called Approximation, Abstraction and Apartness in Automata Learning (APPLE). For details, see
here and feel free to contact us with any questions about the project and position. You will be supervised by
Jurriaan Rot.
The start of the project is flexible (sometime between November and the summer of 2024). If you have not yet fully completed your Master's degree but are excited about this position, please do not hesitate to apply or contact us.
Your teaching load may be up to 10% of your working time.