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Open PhD position

鈥淒eep learning based variable selection with automated feature design and uncertainty estimation鈥�

Start: now

Area
Recently, intelligent machine learning methods, in particular deep learning methods, are becoming increasingly important for semiconductor test. The Graduate School Intelligent Methods for Test and Reliability () at University Stuttgart, in cooperation with the world-leading company in semiconductor test, studies topics such as design for test and diagnosis, post silicon validation, test generation and optimization, robust device tuning, system-level test, lifetime test, and test automation.

Topic
This open PhD position is located in Phase 2 of GS-IMTR. It will continue the successful project 鈥淒eep learning based variable selection for post silicon validation鈥� in Phase 1 of GS-IMTR (see arXiv:2010.13631 for a first idea). The main goal is to extend the previous study in two directions: a) generate importance scores for input variables with uncertainty information as reliability measure, b) in addition to variable selection, search on relevant analytic expressions of input variables for a certain downstream task via combinatorial optimization or reinforcement learning. It is a topic involving deep learning and integer optimization.

Requirements:

• High interest on this topic
• High鈥損erformance Master degree in related areas (e.g. EE, CS, Math, ..)
• Solid knowledge in deep learning and experience in Pytorch/Tensorflow
• Solid knowledge in optimization
• Interest on semiconductor test and on teamwork

At the University of Stuttgart, we actively promote diversity among our employees. We have set ourselves the goal of recruiting more women scientists and employing more people with an international background, as well as people with disabilities. We are therefore particularly pleased to receive applications from such people. Severely disabled people are given preference if they have the same professional qualifications.

In case of interest, please contact Prof. Bin Yang (bin.yang@iss.uni-stuttgart.de) by sending complete CV and transcripts of Bachelor and Master.