Postdoc on artificial intelligence for electromagnetic risk management

KU Leuven
Brugge, Belgium
3 months ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Job source

Tech stack

Artificial Intelligence Data Analysis Systems Engineering Artificial Neural Networks Python (Programming Language) Machine Learning Tensorflow Information Technology

Job description

The rapid evolution of automotive and high-reliability electronics demands faster, smarter and more robust integrated circuit (IC) design methodologies. In this research project, a leading research university collaborates with an industrial semiconductor company to push the boundaries of electromagnetic compatibility (EMC) by developing an AI-driven Intelligent Diagnostic Assistant (IDA) for IC design and verification.

As a postdoctoral researcher, you will play a key scientific role in this project and:

  • Conduct advanced research on AI- and data-driven methods for EMC-aware IC design and verification, with a strong focus on risk-based reasoning.
  • Contribute to the development of the Intelligent Diagnostic Assistant (IDA) that integrates machine learning models, expert EMC knowledge, and explainable AI techniques.
  • Design and validate predictive models (e.g. graph neural networks, physics-informed ML, hybrid AI approaches) to assess EMC risks in integrated circuits.
  • Translate EMC expertise, simulation results, and measurement data into structured representations suitable for machine learning and automated reasoning.
  • Collaborate closely with academic researchers and engineers from industrial partners, ensuring strong alignment between research outcomes and industrial needs.
  • Publish research results in leading international journals and conferences in the fields of EMC, microelectronics, and artificial intelligence.
  • Support the supervision of PhD researchers and contribute to knowledge transfer within the research group.
  • Participate actively in project meetings, workshops, and dissemination activities at national and European level., 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.

Requirements

Do you have experience in Research?, Do you have a Doctoral degree?, * A PhD degree in Electrical Engineering, Applied Physics, Computer Science, or a closely related field.

  • Strong research experience in electromagnetic compatibility, integrated circuits, RF/analog/mixed-signal design, or related domains.
  • Proven expertise or strong interest in machine learning and AI, preferably applied to physical or engineering systems.
  • Experience with modeling, simulation, and data analysis, and solid programming skills in Python (experience with ML frameworks is a plus).
  • Ability to bridge fundamental research and industrial application.
  • A strong or emerging publication record.
  • Excellent English communication skills.
  • A proactive, independent researcher with strong collaboration skills.

Benefits & conditions

  • A fully funded 2-year Postdoc (possibly extendable).
  • 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 Postdocs, PhD students and test engineers.
  • Close collaboration with industrial and academic partners in an international setting.

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