PhD position on Yield and Variability Optimization of Integrated Circuits using Machine Learning

Gent - PhD
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Meer dan twee weken geleden

The goal of the PhD research is using Machine Learning (ML) algorithms to optimize the production yield of modern integrated circuits (ICs).

IDLab is a core research group of imec, a world-leading research and innovation hub in nanoelectronics and digital technologies, with research activities embedded in 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.

What you will do

The goal of the PhD research is using Machine Learning (ML) algorithms to optimize the production yield of modern integrated circuits (ICs). In particular, we are looking for a PhD candidate that will work on combining data-driven and model-driven approaches (hybrid ML) to optimize the yield of manufactured ICs in a cleanroom environment. The proposed PhD research is defined within the context of several national and international research projects on the integration of machine learning methodologies into the design and analysis of modern ICs. The successful candidate will cooperate with enthusiastic colleagues and diverse external partners to fulfil the project requirements, while staying up to date with important changes in the related literature.

Who you are

  • You have (or will obtain in the next months) a master degree in Computer Science, Electronics-ICT, Informatics (Mathematical), or equivalent, with excellent ('honors'-level) grades.
  • You are interested in and motivated by the research topic, as well as in obtaining a PhD degree.
  • You have an open mind and a multi-disciplinary attitude.
  • You have a strong interest in machine learning, and are eager to advance the state of the art. Experience with machine learning algorithmic approaches or frameworks (such as PyTorch and Tensorflow) is considered a plus.
  • You have a strong interest in the design of complex electromagnetic and electronics systems. Knowledge of the manufacturing process of modern ICs is considered a plus.
  • You have excellent analytical skills to interpret the obtained research results.
  • You are a team player and have strong communication skills.
  • Your English is fluent, both speaking and writing.

What we do for you

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 PhD 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 PhD. This PhD position is available starting fall 2019.

Interested?

Send your application by email or any questions concerning this vacancy to prof. Tom Dhaene (tom.dhaene@ugent.be) and dr. Domenico Spina (domenico.spina@ugent.be), indicating “Job Application: Yield Optimization via ML” 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 Skype) as a first contact in a multi-stage selection process.
·       Application deadline: 30/7/2019 or until the vacancy is filled.
·       Type of contract: Full-time
·       Employment: Temporary (4 years), with yearly progress evaluation
·       Starting date: Fall 2019

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