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Kevin Mets

3 months ago

Doctoral scholarship holder in Artificial Intelligence: Continual Reinforcement Learning University of Antwerp in Belgium

Degree Level

PhD

Field of study

Machine Learning

Funding

Available

Deadline

Expired

Country flag

Country

Belgium

University

University of Antwerp

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Keywords

Machine Learning
Deep Learning
Smart Grid Technology
Artificial Intelligence
Computational Science
Python Programming
Reinforcement Learning
Robotics
Autonomous System
Informatic

About this position

The University of Antwerp is offering a full-time doctoral scholarship holder position in Artificial Intelligence, focused on continual reinforcement learning. The work sits within the Department of Electronics and Information and Communication Technology in the Faculty of Applied Engineering, with research aligned to IDLab and reinforcement learning at the University of Antwerp.

This PhD project studies how learning agents can adapt across changing tasks, environments, or objectives while retaining previously learned skills. The topic addresses key challenges in modern AI such as catastrophic forgetting, knowledge transfer, and learning under non-stationarity. Potential application domains include autonomous systems, robotics, and flexible asset management in smart grids (for example storage and electric vehicles).

The doctoral researcher will prepare and defend a PhD thesis, publish scientific articles, and undertake a limited amount of teaching and research support for the department. The position is student-oriented but research-intensive, making it suitable for applicants with a strong background in machine learning and a clear interest in developing new algorithms for continual and reinforcement learning.

Eligibility highlights: applicants should hold, or be close to obtaining, a master's degree in Computer Science, Informatics, Mathematics, or a closely related field. Strong academic performance is expected. The university also asks for a solid interest in AI and machine learning, experience with deep learning, preferably reinforcement learning experience, practical Python coding skills using frameworks such as PyTorch, strong analytical ability, and proficiency in English.

Funding: the university offers a doctoral scholarship for one year, renewable annually for up to three further years after positive evaluation. The scholarship amount follows the standard doctoral scholarship scale, and additional benefits include eco vouchers, internet-connectivity allowance, and either a bicycle allowance or full reimbursement of public transport costs for commuting.

Work location: most work will be carried out at City Campus (The Beacon) and Campus Groenenborger in Antwerp, Belgium.

Application deadline: 16 August 2026 at midnight Brussels time. Applications must be submitted online and include a motivation letter, academic CV with transcript of study results, two reference letters, an English proficiency certificate, and a short research proposal (maximum four pages, excluding references) covering the problem statement, novelty/impact, approach, evaluation plan, and alignment with IDLab research.

Funding details

Available

What's required

Applicants must hold a master's degree in Computer Science, Informatics, Mathematics, or a closely related field, or expect to have obtained it by the start date. Candidates should have excellent study results, strong interest in machine learning/artificial intelligence, experience with deep learning, preferably reinforcement learning, and coding experience in Python with deep learning toolkits such as PyTorch. Good analytical and problem-solving abilities are required, along with proficiency in English (written and spoken). Teaching competences should align with the University of Antwerp’s educational vision, and research qualities should align with the faculty and university research policies.

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Professors

Assistant Professor at University of Antwerp

University Name
Country flag

Belgium

Research Interests

Artificial Intelligence

Polymer Chemistry

Python Programming

Reinforcement Learning

Optimal Control