Postdoc in combinatorial search & Optimization (SAT / CP / SMT / MIP /....)

We are looking for an excellent PhD Researchers to strengthen Bart Bogaerts' research group on Knowledge Representation and Combinatorial Optimization, which is part of the Declarative Languages and Artificial Intelligence (DTAI) section of the department of Computer Science of KU Leuven.

Responsibilities

The field of combinatorial optimization is concerned with developing generic tools that take a declarative problem description and  automatically compute an optimal solution to it. Often, users specify their problem in a high-level, human-understandable formal language.  This specification is first translated into a low-level specification a solver understands and subsequently solved. Thanks to tremendous  progress in solving technology, we can now solve a wide variety of NP-hard (or worse) problems in practice. Moreover, these tools are  increasingly used in real-life applications, including high-value and life-affecting decisions. Therefore, it is of utmost importance that they be  completely reliable.

One of the central objectives of our research group is to develop methodologies and tools with which we can guarantee with 100%  certainty that the right problem has been solved correctly.  To achieve this ambitious objective, we will build on recent breakthroughs in proof logging, where solvers do not just output an answer,  but also a machine-verifiable proof (or certificate) of correctness. However, a major limitation of current techniques is that correctness is  not proven relative to the human-understandable specification written by the user, but relative to the low-level translation that the solver  receives, meaning that there is no guarantee that the solver is solving the original problem. In this project, we will investigate end-to end  guarantees of correctness. When successful, this will have a major impact on the way combinatorial optimization software is developed,  evaluated, and used: the proofs produced will enable (1) debugging, since proofs contain detailed information about where bugs occurred,

(2) auditability, since proofs can be stored and checked by an independent third party, and even (3) rigorous evaluation of algorithmic  improvements.

For some inspiration on this topic, see the CertiFOX project page:  https://www.bartbogaerts.eu/projects/CertiFOX/

Another long-standing objective is to develop methods by which we can explain the reasoning that leads to a certain decision made by  combinatorial optimizer in a human-understandable way. This can range from explaining why a problem has no solutions, to explaining  why a solution is optimal, or why a problem has a unique solution and how a user could have seen this.  Explanations are crucial for building trust in declarative solutions and for future-proofing our tools for new laws and regulations such as the  GDPR, which requires that all AI with an impact on human lives needs to be accountable.

Examples of such explanations in a puzzle domain can be found on the ZebraTutor webpage  https://bartbog.github.io/zebra/

The selected candidate will do research that fits well with the above described topics.

Profile

• Candidates should hold (before start-date of the position) a PhD degree in computer science (or a closely related field)
• Proficiency in English is required.
• Research-wise, strong expertise in combinatorial optimization algorithms (e.g., SAT solving, constraint solving, MaxSAT, SAT modulo theories, Answer Set Programming, (Mixed) Integer Programming, ...) is required.

Offer

Fully-funded postdoc position, with expected duration of 2 years (the length is negotiable) and sufficient travel budget.

Interested?

For more information please contact Prof. dr. Bart Bogaerts, mail: bart.bogaerts@kuleuven.be.

You can apply for this job no later than October 30, 2026 via the online application tool

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