Research Associate (m/f/d) for Neuromorphic Computing in Edge Systems full-time

Environment

Do you want to actively shape the future of intelligent embedded systems and artificial intelligence in research and advance development? Your tasks include the development of novel and innovative methods such as "event-based computing" and "near-memory computing" for the execution and acceleration of neuromorphic artificial intelligence (AI), in which findings about biological neural networks are transferred into approaches for technical systems.

The CeCaS project

Automated, networked and electrified vehicles are picking up speed. However, energy-efficient and cost-effective high-end computing platforms are lacking for full suitability for everyday use. AI-based topics in particular require customized, real-time capable and energy-efficient high-performance processors.

CeCaS creates the processor and software basis for heterogeneous real-time-capable high-performance central computers in vehicles. In short: automotive supercomputing.

One aspect is research into new approaches for the execution and acceleration of neuromorphic artificial intelligence (AI). This includes the development of hardware accelerator architectures and the associated software environment in order to develop the next generation of highly energy-efficient co-processors.

In this environment, a wide range of questions arise that you can immediately address as a doctoral student with Prof. Dr.-Ing.

Tasks

• You will work on research and development contracts as well as publicly funded research projects.
• You will be responsible for the development of novel and innovative solutions for the next generation of AI-based systems by integrating Spiking Neural Networks (SNN) in embedded systems, for example in intelligent intersection systems for automated and autonomous driving, for processing biosignals and for predictive maintenance.
• You present and reflect on the results with technical experts. Publications and exchange with the scientific and business community are expressly desired.

Prerequisites

• You have a Master's degree in electrical engineering, computer science, mechatronics, physics or a related field of study.
• You have a technical understanding.
• You have experience with established embedded software and hardware tools and methods (e.g. Linux, VHDL, C, Python) and in the field of machine learning.
• Ideally, you have experience with FPGAs or knowledge in the field of neuromorphic computing.
• You are motivated and committed, think and work independently.
• You have a very good command of German or English.

How to apply

Have we sparked your interest? Then apply to us.
Here is the link to the FZI job advertisement: https: //karriere.fzi.de/Vacancies/401/Description/1
We look forward to getting to know you!

If you have any questions, please send your documents by e-mail to Dr.-Ing. Victor Pazmino Betancourt / pazmino∂fzi.de