Open Positions - Student projects/jobs (flexible topics)
You can also find some additional student projects/jobs (precise topics) on this webpage.
The EMT group is looking for
- Multiple bachelor/master students interested in developing open-source EDA cloud platforms. Please contact Ms. Jiahui Peng with your CV for more information
- Two bachelor/master students with good programming skills and are interested in math or data analysis. Please contact Ms. Meng Lian with your CV for more information.
- Multiple bachelor's/master's students with good programming skills, web development experience, math and mathematical modeling skills, or with an informatics background. Please contact Dr.-Ing. Yushen Zhang, with your CV for more information.
- Multiple bachelor/master students to work on photonic device simulation. Experience with tools like Tidy3D, MEEP, COMSOL, or OptSim is highly desirable. Please contact Ms. Liaoyuan Cheng with your CV for more information.
- Multiple master students to work on modeling and predictive analysis of photonic devices. Experience with Ansys Lumerical is highly desirable. Students with a background in photonics, networks-on-chip, or related fields are encouraged to apply. Please contact Dr.-Ing. Zhidan Zheng (zhidan.zheng@tum-create.edu.sg) with your CV for more information.
Open Positions - Doctoral Candidates
The Chair of Electronic Design Automation at TUM (TUM-EDA) currently offers two open PhD positions.
Currently, we are conducting research on a range of topics, including design, simulation, and modeling methodologies for next-generation hardware for edge computing platforms.
1. Open Doctoral Candidate Position for EDA for Machine Learning: Tools for HW/SW Codesign of Highly Heterogeneous Edge AI Systems
Expected starting date: January 01, 2027 - December 31, 2029
Duration: 3 years with a possible extension
TUM-EDA offers new doctoral candidate positions in EDA for Machine Learning, within the domain of tools for HW/SW Co-design.
This new doctoral candidacy aims to explore integrated tooling frameworks for the efficient modeling and deployment of Neural Network (NN) workloads to edge computing platforms:
- ML Compilers, including Frontend, Midend, and Backend Compiler techniques.
- Exploration of functional and non-functional simulation techniques for the rapid evaluation of NN workloads and edge computing platforms.
- Design Space Exploration (DSE) and Optimization for the Codesign of HW/SW solutions.
The potential candidate should be familiar with digital circuits and its design tools (HW and SW Compilers and EDA flows). In addition, the candidate should have a good understanding of neural networks or is strongly interested in exploring machine learning methods.
2. Open Doctoral Candidate Position for Next Generation Compilation for Extreme Edge AI Systems (TinyML)
Expected starting date: January 01, 2027 - December 31, 2029
Duration: 3 years with a possible extension
TUM-EDA offers new doctoral candidate positions in EDA for Machine Learning (ML) within the domain of ML Compilers for customized hardware platforms.
This new doctoral candidacy aims to explore the integrated tooling framework of efficiently modeling and deploying neural network workloads to edge computing platforms:
- ML Compiler systems spanning over multiple stages: Frontend (Model import, Neural Architecture Search, Quantization), Midend (Partitioning, Fusing), and Backend (code generation for target hardware).
- Research with and contributions to open-Source tools and frameworks.
- Design Space Exploration (DSE) and Optimization for the Customization of hardware platforms (RISC-V).
The potential candidate should be familiar with classical SW (LLVM, Clang) and ML (TVM, IREE, …) compilers, as well as Embedded Systems, preferably RISC-V systems.
In addition, the candidate should have a good understanding of neural networks or is strongly interested in exploring machine learning methods.
If you are interested in this position, please contact us mailto:office.eda@xcit.tum.de with your CV, transcripts, and referral letters.