Assistant Professor In Formal Methods and Neuro-Symbolic AI

Assistant Professor In Formal Methods and Neuro‑Symbolic AI Departments, Department of Mathematics and Computer Science Introduction

Are you motivated by mission-driven research
? Are you eager to design and analyze next‑generation autonomous, self‑healing systems? Are you excited to help shape one of the fastest‑growing and most transformative fields of our time? Are you interested in translating vision of zero‑touch autonomous systems into tangible and actionable research goals, and willing to demonstrate and validate Zero‑touch systems in practice? If so, we would welcome the opportunity to assess your potential fit for this role, which forms part of a broader recruitment effort involving three assistant professor vacancies.

The Department of Mathematics and Computer Science is in the process of establishing a mission‑driven and impactful collaboration on research and technology challenges of Zero‑touch Systems. In this context, the department has a total of three vacancies at the assistant professor level. The mission of this joint multi‑disciplinary research collaboration is to design, develop, verify, and validate the self‑healing, autonomous systems of the future.

This can only be achieved through a close interaction of various disciplines to ensure resiliency, intelligence, adaptation, security, and trust by design. The research is dedicated to the development of proven, integrated, and demonstrable solutions that advance autonomous systems to a level where their performance is rigorously always guaranteed and can be reliably trusted by humans.

The Zero‑touch Systems research is expected to have significant societal impact and requires collaborative multi‑disciplinary research to be successful. We expect outcome of this research to facilitate research, industry, and institutes nationally and worldwide to deploy and adopt the obtained research‑based solutions. Participation in this research therefore requires strong commitment to work on impact‑focused research, addressing scientific, technical and socio‑technical challenges of Zero‑touch systems.

We expect successful candidates to develop this mission‑driven research program and create tangible societal impact through their research on, e.g.:

• Developing Agentic AI for enabling full autonomy of systems
• Developing “future‑proof” methods for software engineering for autonomous systems
• Developing new methods to guarantee the safety and reliability of zero‑touch systems

You will be evaluated on your achievements with respect to objectives and KPIs of the project and not only on traditional research metrics such as publications. You also play a leading role in building a community within and beyond the Department around the research questions and challenges of Zero‑touch Systems.

The Formal System Analysis (FSA) Cluster studies formal techniques to model and analyze software and hardware systems. The goal is to develop techniques that are both mathematically elegant and practically effective. Techniques typically used are term rewriting, fixpoint logics, parity games, Markov models, SAT and SMT solving and symbolic state space representations. The group maintains several tools sets, such as mCRL2, Storm and Stark, which are used at several major industries and public bodies, generally as verification backends.

The group has strong ties with industrial partners and actively strives to apply its methods in industrial contexts, fueling the economic and societal impact of the group.

This role is part of the FSA cluster within the department of Mathematics and Computer Science. Artificial Intelligence plays a central role in Zero‑touch systems, enabling fast decision making in the many situations AI‑enabled systems operate. However, ensuring reliability in uncertain or dynamic environments remains a significant challenge. The field of Formal Methods deals with theory and techniques for providing such guarantees.

This position focuses on the interdisciplinary area of Formal Methods and AI, developing theory and techniques to improve the reliability of autonomous and self‑adapting systems by combining benefits from Formal Methods with the…