The M-Group at KU Leuven Bruges Campus is an interdisciplinary research team focusing on intelligent and dependable mechatronic systems, combining research expertise from the departments of Computer Science, Electrical Engineering and
Mechanical Engineering. Two of the key research tracks focus on the application of Artificial Intelligence and Machine Learning in real-world industrial settings on the one hand, and electromagnetic risk management on the other hand. The objective of this PhD position is to explore the synergies at the intersection of both domains. More specifically, the position focusses on researching novel AI techniques for anomaly detection in electromagnetic environments. The successful candidate will be offered to opportunity to pursue a PhD in Computer Science at KU Leuven, and will also be embedded within the Declarative Languages and Artificial Intelligence (DTAI) lab (https://dtai.cs.kuleuven.be), which pursues excellence in an explicit and synergistic combination of fundamental and applied research on machine learning and artificial intelligence. Doctoral training is provided in the framework of the Leuven Arenberg Doctoral School (https://set.kuleuven.be/phd). The PhD will be supervised by Prof. Mathias Verbeke and Prof. Tim Claeys.
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Project
This PhD position is part of the PATTERN Doctoral Network, which is built on the conviction that AI can significantly enhance holistic EMI (electromagnetic interference) solutions. By uncovering previously unknown patterns in the complex interactions between humans, devices, and their environments, AI can support the development of safe, sustainable, and design-forward strategies for mitigating EMC challenges. PATTERN unites expertise across four critical areas: electromagnetic compatibility (EMC), medical engineering, risk management, and sustainability management, driving innovation toward a safer, smarter, and more sustainable future.
In this PhD project, the aim is to leverage AI for real-time anomaly detection in the electromagnetic environment, where anomalies are rare deviations from normal behavior. Due to the challenge of gathering a comprehensive dataset of anomalies, an unsupervised or semisupervised approach will be devised, using normal data to construct a model representing regular electromagnetic behavior. The project will combine EMI footprints, which capture normal variations through characteristic curves and statistical distributions, with state-of-the-art machine learning and deep learning techniques (e.g., one-class support vector machines, autoencoder- and transformer-based architectures) to detect anomalies. By integrating these AI techniques with EMI footprints, the objective is to develop an efficient methodology for real-time anomaly detection in the electromagnetic environment, resulting in better insights towards the underlying root cause.
Profile
We are seeking a highly motivated, enthusiastic, passionate, and communicative researcher, with a proactive and creative attitude who is eager to explore innovative solutions. If you recognize yourself in the story below, then you have the profile that fits the project and the research group:
- I have a Master's degree in Computer Science, Artificial Intelligence, Electrical Engineering, or a related field and performed above average in comparison to my peers.
- I am proficient in written and spoken English.
- During my courses or prior professional activities, I have gathered experience with machine/deep learning methods and/or EMC, and can demonstrate a strong affinity with (one of) these fields. Prior experience with time series data analysis and/or anomaly detection is a plus.
- I am proficient in Python and am familiar with data science and machine/deep learning toolkits.
- As a PhD researcher at KU Leuven, I perform research in a structured and scientifically sound manner. I read technical papers, understand the nuances between different theories and implement and improve methodologies myself.
- Based on interactions and discussions with my supervisors and the colleagues in my team, I set up and update a plan of approach for the upcoming 1 to 3 months to work towards my research goals. I work with a sufficient degree of independence to follow my plan and achieve the goals. I indicate timely when deviations of the plan are required, if goals cannot be met or if I want to discuss intermediate results or issues.
- In frequent reporting, varying between weekly to monthly, I show the results that I have obtained and I give a well-founded interpretation of those results. I iterate on my work and my approach based on the feedback of my supervisors which steer the direction of my research.
- I feel comfortable to work as a team member and I am eager to share my results to inspire and being inspired by my colleagues.
- I value being part of a research group which is well connected to the mechatronics industry and I am eager to learn how academic research can be linked to industrial innovation roadmaps.
- During my PhD I want to grow towards representing the research group on project meetings or conferences. I see these events as an occasion to disseminate my work to an audience of international experts and research colleagues, and to learn about the larger context of my research and the research project.
We encourage candidates from diverse backgrounds and experiences to apply, as we believe that different perspectives contribute to better research and innovation.
Application Instructions for the PhD vacancy
To apply for this position, please use the online application tool and ensure that you submit the following documents in a single PDF file:
- Motivation Letter: A letter (maximum 1 A4 page) addressing your strengths and qualifications in relation to the project.
- Complete Academic CV: A detailed CV including information about your education, current position, work experience (if any), employment gaps (if any), interests, extracurricular activities, international experiences, and projects demonstrating your programming/software skills, background knowledge relative to the project and level of expertise.
- List of Publications: If applicable, provide a list of your publications, including DOIs. Please do not include PDFs of the publications.
- Copies of Diplomas: Include copies of your BSc and MSc degrees.
- Transcript of Records: Provide transcripts for your BSc and MSc degrees. If you have not yet completed your Master's degree, include your available credits and scores, as well as a list of courses you are taking in the upcoming semester.
- English Summary of Master Thesis: A summary of your master thesis in English (maximum 1 A4 page, or 2 pages max when including a figure).
- Proof of English Language Proficiency: Documentation demonstrating your proficiency in English (TOEFL, IELTS, …), if available.
- Reference Letter or Contact Details: A reference letter or the contact information for one reference who can provide a recommendation letter upon request.
Selection Process and Timeline
- Application Deadline: March 31, 2024. Applications will be accepted until the position is filled.
- Shortlisting: Candidates will be shortlisted based on their submitted documents.
- Interviews: Shortlisted candidates will be invited for an interview, which may be conducted online or in person.
Offer
This is more than just a PhD position - it’s an opportunity to be part of an internationally recognized network of researchers through the MSCA PATTERN Doctoral Network (https://pattern-dn.eu). As a PhD candidate, you will be based at KU Leuven’s Bruges Campus, with exciting secondments at Thales (NL) and IETR (CNRS, FR).
We offer:
- A fully funded 3-year PhD scholarship (extendable to 4 years), with a remuneration package competitive with industry standards in Belgium, a country with a high quality of life and excellent health care system.
- Ample occasions to develop yourself in a scientific and/or an industrial direction. Besides opportunities offered by the research group, further doctoral training for PhD candidates is provided in the framework of the KU Leuven Arenberg Doctoral School (https://set.kuleuven.be/phd), known for its strong focus on both future scientists and scientifically trained professionals who will valorise their doctoral expertise and competences in a non-academic context.
- Opportunities to collaborate in groundbreaking research and participate in international conferences.
- Access to state-of-the-art infrastructure and a range of university benefits (health insurance, etc.).
- A dynamic, passionate team of fellow PhD students and test engineers.
In the context of the MSCA Doctoral Network PATTERN, KU Leuven receives Researcher Allowances, consisting of a living allowance, mobility allowance and, if applicable a family allowance, for the recruitment of the Doctoral Candidate. These Researcher Allowances will be used to cover the Doctoral Candidate’s remuneration, including all employer and employee’s taxes and contributions.
The position will be hosted within the collaborative and internationally oriented research environment at KU Leuven, one of the world's leading universities (ranked among the top 100 globally). Founded in 1425, KU Leuven has been a center of learning for nearly six centuries and is Belgium’s highest-ranked university, as well as one of the oldest and most renowned universities in Europe. KU Leuven provides a truly international experience, high-quality education, world-class research, and cutting-edge innovation, having topped Reuters' ranking of Europe's most innovative universities for four consecutive years.
The successful candidate will be encouraged to present their research at international conferences and national events, with a strong emphasis on publishing high-quality conference papers and journal articles. They will benefit from our robust international research and industrial network, which is actively involved in this project.
KU Leuven Campus Bruges, located in the magnificent medieval city of Bruges in West Flanders, offers a vibrant academic setting in close proximity to a network of companies. The campus features newly established labs to support both educational and research needs.
https://pattern-dn.eu
Interested?
For more information please contact Prof. Mathias Verbeke, tel.: +32 50 66 47 76, mail: mathias.verbeke@kuleuven.be or Prof. Tim Claeys, mail: tim.claeys@kuleuven.be.
KU Leuven strives for an inclusive, respectful and socially safe environment. We embrace diversity among individuals and groups as an asset. Open dialogue and differences in perspective are essential for an ambitious research and educational environment. In our commitment to equal opportunity, we recognize the consequences of historical inequalities. We do not accept any form of discrimination based on, but not limited to, gender identity and expression, sexual orientation, age, ethnic or national background, skin colour, religious and philosophical diversity, neurodivergence, employment disability, health, or socioeconomic status. For questions about accessibility or support offered, we are happy to assist you at this email address.
