Research Fellow/PhD Student (f/m/d)
Intelligent Architectures for Future Machine Learning
Institute for Computer Engineering (ITEC)
In the Department of computer science at the Institute of Computer Engineering, Chair for Embedded Systems (CES), we focus our research on neural network, computer architecture, and emerging technologies. As a matter of fact, computing systems have reached a point, where significant improvements in computational performance and efficiency have become profoundly hard to achieve. When it comes to Deep Neural Networks (DNNs), existing architectures are severely bottlenecked by the massive amount of data that must be transferred, which goes far beyond the communication capability of our modern systems.
In this project, we will investigate how recent breakthroughs in architectures, in which novel concepts of near-memory and in-memory computing are employed, leads to more efficient and higher performance training and inference of deep neural networks. We will also explore how innovations in the underlying technologies, in which advanced Non-Volatile Memory (NVM) devices, are in use open doors for more intelligent machine learning in the future.
as soon as possible
The following qualifications are required seeking your consideration for this position.
- MSc degree (or equivalent) in Electrical Engineer or Computer Science.
- Good English skills.
Skills in one or more of the following areas is recommended
- Hardware accelerators for neural networks.
- Computer architecture
- FPGA-based Designs.
- Programming skills in C/C++, or scripting languages like Python
Salary category 13, depending on the fulfillment of professional and personal requirements.
limited to 3 years and can be extended for two more years
Application up to
Contact person in line-management
For further information, please contact Dr. Hussam Amrouch, firstname.lastname@example.org, Tel: +49(0)721/608-45733.
Please apply online using the button below for this vacancy number 1023.
If qualified, severely disabled persons will be preferred.
Personnel Support is provided by:
Phone: +49 721 608-42016,
Kaiserstr. 12, 76131 Karlsruhe