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Application Deadline: January 29, 2023.
The Graduate School “Intelligent Methods for Semiconductor Test and Reliability” (GS-IMTR) at the University of Stuttgart in cooperation with ADVANTEST invites in its second funding phase applications for 10 full-time PhD positions (research assistant, 100%TV-L E13) for 3 years starting in April 2023.
The GS-IMTR is an interdisciplinary graduate school combining research expertise from computer science, electrical engineering, information technology, and beyond. GS-IMTR is Germany‘s first industry-funded Graduate School, established in cooperation with ADVANTEST, a major global manufacturer of automatic test equipment for integrated circuits. Its overall aim is to develop new methods for topics such as design for test and diagnosis; postsilicon validation; test generation and optimization; robust device tuning; system-level test; lifetime test and reliability management; security, privacy and reliability of testing; test for advanced and emerging technologies and test automation. A modern understanding of these topics demands novel artificial intelligence methods and has tight connections to data science, data analytics, data understanding, machine learning, security and privacy.
A structured doctoral program includes a supervision concept, mentorship from Advantest, measures for international mobility and a research stay abroad, as well as a tailored qualification program with subject-based and soft-skill courses.
Positions are being offered in the following 10 research projects, for details about each project see below:
- VirtualTest for mixed-signal Circuits: DigitalTwin based Development of Post-SiliconTests
- EnhancingTest Methods by Magnetic Fields
- Over-the-Air (OTA) production-test concepts for future millimeter-wave antenna array modules
- Intelligent Sensing and On-Chip Learning for Silicon Lifecyle
- Test and Reliability Challenges for Advanced Sub-5nm Technologies
- NovelTest Methods for Emerging and Classical Memories using Magnetic Field
- Variable selection with automated feature design for post-silicon validation and production
- Automatic and DynamicTuning beyond Post-Silicon Validation
- Explanations for Failures from SoftwareTesting
- Privacy-Preserving Machine Learning for Semiconductor Testing
- Applicants should hold a master’s or equivalent degree in electrical engineering, computer science, information technology, mathematics, physics, or a related discipline with above-average results.
- They are expected to show a high level of proficiency in both spoken and written English.