Last application date Jun 30, 2023 00:00
Department TW05 - Department of Information Technology
Contract Limited duration
Degree Master in Computer Science, Mathematics, Informatics, Engineering, or equivalent
Occupancy rate 100%
Vacancy type Research staff
PhD Student Machine Learning for engineering (ML4ENG)
IDLab, Ghent University - imec, Belgium
IDLab is a core research group of imec, a world-leading research and innovation hub in nanoelectronics and digital technologies, with research activities at Ghent University. IDLab performs fundamental and applied research on data science and internet technology, and is, with over 300 researchers, one of the larger research groups at imec. Our major research areas are machine learning and data mining; semantic intelligence; multimedia processing; distributed intelligence for IoT; cloud and big data infrastructures; wireless and fixed networking; electromagnetics, RF and high-speed circuits and systems.
Job description
The activities of the Ph.D. position are embedded in this stimulating environment with a focus on data-efficient machine learning (or surrogate modeling) techniques to solve complex and challenging engineering problems with use cases from various engineering disciplines such as electrical and mechanical engineering.
In particular, the goal of the Ph.D. research is to design tools and techniques to improve key parts of the engineering design pipeline, aiding designers in several computer-aided design (CAD; design and analysis of computer experiments) activities such as uncertainty quantification, calibration, sensitivity analysis, design space exploration, and optimization, etc. Machine learning algorithms such as Gaussian Processes and Bayesian optimization will be used to create a modern design flow, e.g., for the efficient design of electromagnetic and electronics circuits. This includes techniques for physics-informed modeling, generative design, preference-based learning, explainable engineering design, spatiotemporal modeling, free-form topology and shape optimization, efficient data collection, labeling, etc. The proposed Ph.D. research is defined within the context of several national and international research projects on automation in machine learning (AutoML).
For more information on the research see https://sumo.intec.ugent.be/home
We are looking for highly creative and motivated Ph.D. students with the following qualifications and skills.
Our offer
We offer the opportunity to do full-time research in an international (with over 17 nationalities at IDLab, part of imec and Ghent University) and friendly working environment, with a competitive salary at Ghent University. While grounded in fundamental academic research, as a Ph.D. candidate you will also participate in collaborative research with industrial and/or academic partners in Flanders and/or on a wider geographic scale (e.g., EU H2020 projects), in the framework of new/ongoing projects. Furthermore, you will publish your research results at major international conferences and in journal papers, as part of meeting the requirements for your Ph.D.
Interested?
Send your application by email or any questions concerning this vacancy to prof. Tom Dhaene (tom.dhaene@UGent.be) and prof. Ivo Couckuyt (ivo.couckuyt@ugent.be), indicating “Job Application: ML4ENG” in the subject. Applications should include (1) an academic/professional resume, (2) a personal motivation letter, and (3) transcripts of study results, and (4) at least two reference contacts. After a first screening, selected candidates will be invited for an interview (also possible via Teams) as a first contact in a multi-stage selection process.
Last application date Jun 30, 2023 00:00Department RE21 - Department of Interdisciplinary Study of Law, Private Law and Business LawContract Limited durationDegree Master of Laws in lawOccupancy rate 100%Vacancy type Research staffJob descriptionT...
Ghent University is one of the top 100 universities in the Dutch language area, with more than 44,000 students and 15,000 staff members.
Visit the employer page