Fully Funded PhD Position in Symbolic Explainability for Logic-Based AI Systems – VUB, Belgium

The Vrije Universiteit Brussel (VUB) in Belgium is inviting applications for a fully funded PhD position focused on symbolic explainability for logic-based artificial-intelligence systems. The position is hosted by the Faculty of Sciences and Bioengineering Sciences in Brussels and combines artificial intelligence, logic, knowledge representation, explainable AI, formal reasoning, and computer science.

This doctoral opportunity is suitable for candidates with backgrounds in computer science, artificial intelligence, mathematics, logic, formal methods, software engineering, data science, or a related discipline. The project addresses how AI systems based on logical reasoning can produce explanations that are understandable, faithful, and useful to researchers, developers, and end users.

Position Details

• Position: Fully Funded PhD Position in Symbolic Explainability for Logic-Based AI Systems

• University: Vrije Universiteit Brussel (VUB)

• Faculty: Faculty of Sciences and Bioengineering Sciences

• Location: Brussels, Belgium

• Research areas: Artificial intelligence, explainable AI, symbolic reasoning, logic-based systems, knowledge representation, formal methods

• Contract: Full-time doctoral research position

• Duration: Normally at least four years for full-time PhD research at VUB

• Funding: Fully funded PhD position

• Application deadline: 15 September 2026, according to the aggregated vacancy listing; verify the live VUB vacancy before applying

Research Focus

Many modern AI systems are difficult to interpret because their predictions are generated by complex statistical models. Logic-based AI systems offer a complementary approach: they represent knowledge explicitly and use rules, constraints, ontologies, or formal inference to derive conclusions.

The PhD project will investigate methods for generating explanations from logic-based AI systems. The research may address how to identify relevant reasoning steps, communicate why a conclusion follows, compare alternative explanations, and ensure that explanations remain faithful to the underlying model.

Potential research directions may include:

• Symbolic explanations for logical inference and rule-based reasoning.

• Explainable knowledge representation and reasoning.

• Abductive, deductive, and contrastive explanations.

• Explanation generation for hybrid neuro-symbolic AI systems.

• User-oriented explanations for complex decision-support systems.

• Formal verification of explanation correctness and faithfulness.

• Explainability metrics for logic-based and AI-assisted systems.

• Explanation interfaces for scientific, industrial, or social applications.

• Integration of symbolic reasoning with machine learning.

The exact formalism may involve description logics, answer-set programming, argumentation, constraint solving, automated reasoning, theorem proving, knowledge graphs, or related methods. Candidates should consult the official vacancy page for the confirmed research plan and supervisor information.

Eligibility and Skills

Applicants should normally hold, or be close to completing, a master’s degree or equivalent in a relevant area. Suitable backgrounds may include:

• Computer science or artificial intelligence.

• Mathematics, logic, or theoretical computer science.

• Software engineering or formal methods.

• Data science or knowledge engineering.

• Cognitive science with strong computational training.

• Electrical or systems engineering with relevant AI experience.

• A related discipline with evidence of mathematical and programming ability.

Useful preparation may include experience with:

• Logic, discrete mathematics, or formal reasoning.

• Knowledge representation and reasoning.

• Automated theorem proving or constraint solving.

• Machine learning and hybrid AI.

• Python, Java, Prolog, or another programming language.

• Knowledge graphs, ontologies, or semantic technologies.

• Formal verification or software correctness.

• Human–computer interaction and explanation design.

Applicants should also have strong analytical skills, research independence, and the ability to communicate technical ideas clearly. Experience with explainable AI, responsible AI, symbolic AI, argumentation, or formal methods would be particularly relevant.

Funding and Benefits

The vacancy is explicitly described as a fully funded PhD position. The exact funding mechanism, salary or doctoral grant, employment percentage, social-security contributions, and contract terms should be verified in the official VUB advertisement.

At VUB, full-time PhD research normally takes at least four years. Doctoral candidates participate in the VUB Doctoral Training Programme, which requires at least 30 training credits in total and at least five credits in each of the programme’s four quadrants.

The VUB doctoral environment may include research seminars, methodological training, transferable-skills courses, teaching or communication activities, conference participation, and interdisciplinary collaboration. Applicants should also check the tuition and registration conditions because VUB states that doctoral candidates may pay tuition fees during the first and last academic years of the PhD.

Why Apply?

This PhD is attractive to candidates who want to work on the foundations of trustworthy and interpretable AI. It may be especially relevant to researchers interested in:

• Explainable and responsible AI.

• Neurosymbolic AI.

• Mathematical logic and automated reasoning.

• Knowledge graphs and semantic systems.

• AI safety and verification.

• Human–AI collaboration.

• Decision-support systems.

• Formal models of scientific or engineering knowledge.

The project may also be relevant to biomedical engineering and computational biomechanics when explainable AI is needed for clinical decision-making, medical imaging, patient-specific modelling, or physics-informed machine learning. For example, symbolic explanations could complement neural predictions by identifying physical constraints, causal relationships, or clinically meaningful rules.

VUB Doctoral Environment

VUB organizes doctoral education through three doctoral schools:

• Doctoral School of Human Sciences.

• Doctoral School of Natural Sciences and Bioscience Engineering.

• Doctoral School of Life Sciences and Medicine.

A PhD in symbolic explainability and logic-based AI would most likely be associated with the Doctoral School of Natural Sciences and Bioscience Engineering, although the exact doctoral-school affiliation depends on the faculty and programme.

VUB’s Doctoral Training Programme is designed to help researchers develop academic and transferable skills. Candidates are expected to work with a promotor, prepare a research plan, register in a doctoral programme, and complete the required training credits.

How to Apply

VUB requires PhD candidates to be invited by a promotor before starting the formal application process. Applicants should therefore review the vacancy, identify the named supervisor or research group, and follow the invitation and application instructions in the official advertisement.

A typical application package may include:

• Academic CV.

• Motivation letter.

• Master’s degree certificate and transcripts.

• Research statement or thesis summary.

• Evidence of programming and mathematical preparation.

• Publication list, if applicable.

• Contact details for academic referees.

• Additional documents requested by the VUB recruitment system.

The motivation letter should explain the applicant’s interest in symbolic explainability and logic-based AI. Strong applications should connect previous work to specific methods such as formal reasoning, machine learning, knowledge representation, theorem proving, constraint solving, or human-centred explanation design.

Applicants with an engineering or applied-AI background should describe a concrete problem in which interpretability, verification, or structured reasoning matters. This could include medical diagnosis, autonomous systems, scientific modelling, industrial optimization, or safety-critical decision support.

Application Link

Apply through the official VUB jobs portal:

VUB PhD Vacancies

The VUB all-jobs portal is also available here:

VUB – All Jobs

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