This fully funded PhD position at KU Leuven focuses on developing uncertainty-aware machine learning techniques for risk mitigation in safety-critical systems. Hosted at the Bruges Campus within the interdisciplinary M-Group, the research bridges computer science, electrical engineering, and mechatronics, with a strong emphasis on the dependability of intelligent systems. The successful candidate will join the Declarative Languages and Artificial Intelligence (DTAI) lab, renowned for its excellence in both fundamental and applied research in machine learning and artificial intelligence. The project is part of the Flanders Make Strategic Basic Research project SAIfety, which aims to enhance the robustness and safety assurance of ML models throughout their lifecycle. The research will focus on uncertainty quantification methods, such as Bayesian and interval neural networks, to ensure reliable operation of ML-enabled safety-critical systems. Application domains include deep learning for computer vision and time-series analysis, with validation on industry-grade demonstrators. The work addresses real-world challenges, such as ensuring that ML models can signal when their predictions are unreliable, enabling systems to take appropriate safety measures in industrial environments. KU Leuven offers a vibrant, internationally oriented research environment, ranked among the top 100 universities globally. The Bruges Campus provides access to state-of-the-art lab facilities and a dynamic team of AI researchers, with strong links to the mechatronics industry. Doctoral training is provided through the Arenberg Doctoral School, supporting both academic and professional development. The position includes opportunities to present research at international conferences, collaborate with industrial partners, and publish in high-impact venues. Applicants should hold a Master's degree in Computer Science, Artificial Intelligence, Electrical Engineering, or a related field, with above-average academic performance. Experience with machine/deep learning, Python, and data science toolkits is required; prior exposure to uncertainty quantification, computer vision, or time series analysis is advantageous. Candidates must demonstrate strong communication skills, independence, and a collaborative spirit. KU Leuven values diversity and encourages candidates from all backgrounds to apply. The position is fully funded for four years, with a competitive remuneration package, health insurance, and university benefits. The application deadline is January 22, 2026. To apply, use the online application tool and submit all required documents in a single PDF. For further information, contact Prof. Mathias Verbeke or Dr. Laurens Devos by email, mentioning [UQ Vacancy] in the subject line.

KU Leuven, one of Europe's most prestigious universities, invites applications for a fully funded PhD position focused on visual defect detection and identification in continuous production processes with minimal human feedback. The successful candidate will join the M-Group at KU Leuven Bruges Campus, an interdisciplinary research team specializing in intelligent and dependable mechatronic systems. The group integrates expertise from Computer Science, Electrical Engineering, and Mechanical Engineering, with a strong emphasis on applying Artificial Intelligence and Machine Learning in industrial contexts. This PhD project aims to develop autonomous, self-learning camera-based systems for quality control in continuous production lines, such as those handling polymer products, textiles, or food. The research addresses the challenge of detecting and identifying rare and subtle defects in highly variable products, where traditional manual dataset labeling and model training are inefficient and often result in poor performance due to data imbalance. The project will focus on creating an inline system capable of learning to detect and classify defects on the fly, leveraging continuous data streams and requiring minimal human intervention for feedback and identification. The successful candidate will be embedded within the Declarative Languages and Artificial Intelligence (DTAI) lab, renowned for its excellence in both fundamental and applied research on machine learning and artificial intelligence. Doctoral training will be provided through the Leuven Arenberg Doctoral School, which offers comprehensive support for both academic and professional development. The PhD is supervised by Prof. Mathias Verbeke and Dr. Matthias De Ryck, and is part of the Flanders Make IRVA project RETINA, which aims to enable low-effort visual defect detection and identification in industrial production processes. The research will be conducted in close collaboration with leading industry partners, providing opportunities for real-world validation and industrial impact. Applicants should have a Master's degree in Computer Science, Artificial Intelligence, Electrical Engineering, Mechanical Engineering, or a related field, with above-average academic performance. Required skills include proficiency in English, experience with machine/deep learning, strong affinity for computer vision, and programming expertise in Python. Familiarity with data science and machine/deep learning toolkits is essential, and experience with model deployment and MLOps tools (such as Dockerization and CI/CD pipelines) is advantageous. The ideal candidate will be proactive, creative, and able to work both independently and collaboratively within a dynamic research team. KU Leuven offers a fully funded 4-year PhD scholarship, a competitive remuneration package, health insurance, and access to state-of-the-art infrastructure and university benefits. The Bruges Campus provides a vibrant academic setting with newly established labs and close proximity to industry partners. PhD candidates are encouraged to present their research at international conferences and national events, with a strong emphasis on publishing high-quality papers and engaging with the broader research and industrial community. To apply, candidates should use the online application tool and submit a single PDF containing a motivation letter, academic CV, list of publications (if any), copies of diplomas, transcripts, English summary of their master thesis, proof of English proficiency, and a reference letter or contact details for a referee. KU Leuven is committed to diversity, inclusion, and equal opportunity, welcoming candidates from all backgrounds. For further information, contact Prof. Mathias Verbeke ([email protected]) or Dr. Matthias De Ryck ([email protected]). The application deadline is February 5, 2026.

The M-Group at KU Leuven Bruges Campus offers a fully funded PhD position focused on the application of reinforcement learning with human feedback for toolpath generation in CAD-to-CAM automation. This interdisciplinary research team combines expertise from Computer Science, Electrical Engineering, and Mechanical Engineering, and is renowned for its work on intelligent and dependable mechatronic systems. The PhD project aims to automate the CAD-to-CAM process in machining, leveraging machine learning, multi-objective optimization, and human feedback to generate and improve work plans for manufacturing parts. The successful candidate will pursue a PhD in Computer Science at KU Leuven, embedded within the Declarative Languages and Artificial Intelligence (DTAI) lab, which is internationally recognized for its research in machine learning and artificial intelligence. Doctoral training is provided through the Leuven Arenberg Doctoral School, offering a robust framework for both academic and industrial career development. The research will utilize product design data (CAD files), historical work plans and process information, and machining quality data, focusing on mainstream machining processes such as turning, milling, and drilling. This PhD position is part of the Flanders Make Strategic Basic Research project AutoCAM, which aims to automate work plan generation for both existing and new parts, supporting operators and production planners in industry. The project offers close collaboration with leading industry partners, access to state-of-the-art lab facilities, and opportunities to present research at international conferences and publish high-quality papers. Applicants should have a Master's degree in Computer Science, Artificial Intelligence, Electrical Engineering, Mechanical Engineering, or a related field, with above-average academic performance. Proficiency in English, experience with machine/deep learning, and strong programming skills in Python are required. Prior experience with reinforcement learning, multi-objective optimization, and CAD/CAM processes is advantageous. Candidates should demonstrate independence, teamwork, and strong communication skills. KU Leuven values diversity and encourages candidates from all backgrounds to apply. The position includes a competitive remuneration package, health insurance, and university benefits. Doctoral training and professional development opportunities are provided, with a strong emphasis on linking academic research to industrial innovation. The Bruges Campus offers a vibrant academic setting in close proximity to industry, with newly established labs and a dynamic team of researchers. To apply, candidates must use the online application tool and submit a single PDF file containing a motivation letter, academic CV, list of publications (if any), copies of diplomas, transcripts, English summary of master thesis, proof of English proficiency, and a reference letter or contact details. For further information, contact Prof. Mathias Verbeke at [email protected]. KU Leuven is committed to equal opportunity, diversity, and an inclusive research environment. The university is ranked among the top 100 globally and offers a truly international experience, world-class research, and cutting-edge innovation.

The M-Group at KU Leuven Bruges Campus invites applications for a fully funded PhD position focused on AI-assisted CAD-to-CAM optimization for machining processes. This interdisciplinary research group brings together expertise from Computer Science, Electrical Engineering, and Mechanical Engineering, with a strong emphasis on intelligent and dependable mechatronic systems. The successful candidate will join the Declarative Languages and Artificial Intelligence (DTAI) lab, renowned for its excellence in both fundamental and applied research in machine learning and artificial intelligence. This PhD project aims to automate the CAD-to-CAM process in machining by leveraging reinforcement learning with human feedback and multi-objective optimization. The research will focus on generating and improving work plans for machining processes such as turning, milling, and drilling, using data from CAD files, historical work plans, and machining quality data. The project is part of the Flanders Make Strategic Basic Research project AutoCAM, offering close collaboration with leading industry partners and opportunities for experimental validation in state-of-the-art lab facilities. The position offers a fully funded 4-year PhD scholarship, with a competitive remuneration package, health insurance, and access to university benefits. Doctoral training is provided through the KU Leuven Arenberg Doctoral School, and candidates will have opportunities to present their research at international conferences and collaborate within a dynamic, international research environment. KU Leuven is one of the world's top 100 universities, offering a vibrant academic setting in the historic city of Bruges. Applicants should hold a Master's degree in Computer Science, Artificial Intelligence, Electrical Engineering, Mechanical Engineering, or a related field, with above-average academic performance. Proficiency in English and experience with machine/deep learning are required; prior experience with reinforcement learning, multi-objective optimization, and/or CAD/CAM is a plus. Candidates should be proficient in Python and familiar with data science and machine learning toolkits. The application requires a motivation letter, CV, transcripts, proof of English proficiency, and a reference letter or contact details for a referee. KU Leuven values diversity and strives for an inclusive, respectful, and socially safe environment. The university encourages candidates from diverse backgrounds to apply. For more information, contact Prof. Mathias Verbeke at [email protected], mentioning [AutoCAM vacancy] in the subject line.