Kevin Mets
3 months ago
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Doctoral scholarship holder in Artificial Intelligence – Explainable Reinforcement Learning (XRL) University of Antwerp in Belgium
Degree Level
PhD
Field of study
Machine Learning
Funding
Available
Deadline
Expired
Country
Belgium
University
University of Antwerp
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About this position
The University of Antwerp is offering a full-time doctoral scholarship holder position in Artificial Intelligence focused on explainable reinforcement learning (XRL). The PhD will be based in the Department of Electronics and Information and Communication Technology within the Faculty of Applied Engineering.
This research project investigates how reinforcement learning agents can make decisions, policies, and learning processes understandable to humans. The goal is to improve transparency, trust, safety, and regulatory compliance in high-stakes decision-making systems. Potential application domains include autonomous systems, robotics, and flexible asset management in smart grids such as storage and electric vehicles.
The successful candidate will work toward the preparation and defence of a PhD thesis, publish scientific articles related to the project, and undertake a limited amount of teaching and research support work for the department. The research is aligned with IDLab research on reinforcement learning at the University of Antwerp.
Applicants should hold, or be about to obtain, a master’s degree in Computer Science, Informatics, Mathematics, or a closely related field. Strong study results are required. The university also looks for candidates with a strong interest in machine learning and artificial intelligence, experience with deep learning, preferably experience with reinforcement learning and explainable/interpretable AI, and coding experience in Python with deep learning toolkits such as PyTorch. Good analytical and problem-solving abilities and proficiency in English are also required.
The scholarship is initially awarded for one year and can be renewed annually for up to three additional years after positive evaluation. The monthly scholarship amount follows the standard doctoral scholarship scale. Additional benefits include eco vouchers, an Internet-connectivity allowance, and either a bicycle allowance or full reimbursement of public transport costs.
The planned start date is 1 January 2027 or as soon as possible thereafter. The position is primarily based at City Campus (The Beacon) and Campus Groenenborger in Antwerp, Belgium.
Applications must be submitted online by 16 August 2026 (midnight Brussels time). Required documents include a motivation letter, academic CV with transcript of study results, two reference letters with contact details, an English proficiency certificate, and a short research proposal of up to 4 pages excluding references. The proposal should cover the problem statement and motivation, expected novelty and impact, brief methodological approach, evaluation plan, and alignment with IDLab research.
For questions about the job itself, the contact person is Kevin Mets, Assistant Professor ([email protected]). General application questions can be sent to [email protected].
Funding details
Available
What's required
Applicants must hold a master’s degree in Computer Science, Informatics, Mathematics, or a closely related field, or obtain it before starting. Candidates should demonstrate excellent study results, strong interest in machine learning/artificial intelligence, experience with deep learning, preferably experience with reinforcement learning and explainable/interpretable AI, coding experience in Python with deep learning toolkits such as PyTorch, good analytical and problem-solving abilities, and proficiency in English (written and spoken). Teaching competences should align with the University of Antwerp’s educational vision, and research qualities should align with faculty and university research policies.
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Professors
Assistant Professor at University of Antwerp
Belgium
Research Interests
Artificial Intelligence
Polymer Chemistry
Python Programming
Reinforcement Learning
Optimal Control
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