Openings at CAS

3 postdoc positions on design of hardware for data fusion acceleration of AI frameworks

Opening for: Postdoc

Status details

Status:Open
Announced:01 Feb 2019
Closing date:30 Apr 2019
Duration:24 months
CAS has 3 postdoc openings in the context of several ECSEL-funded projects (PRYSTINE, SUNRISE, NEWCONTROL).

The focus will be on the design of hardware for data fusion for robust, safe, secure perception and acceleration of AI frameworks for decision making. The postdocs will investigate the applicability of neuromorphic computing architectures, and programmable hardware fabrics (FPGA and/or ASIC designs).

The Circuit and Systems lab (CAS) conducts research on efficient digital hardware design for a broad range of computing use-cases with varying power-performance-cost targets. Outcomes of CAS's activities typically include FPGA and ASIC hardware prototypes, design, and simulation frameworks, as well as virtual prototypes.

Requirements

This postdoc position requires a doctoral degree (or relevant experience) in electronic engineering, computer engineering, or computer science field; or (equivalently) 3 years of expertise on the topics relevant to the position. A successful candidate has significant experience in VLSI digital and Mixed Signal system design, circuit design, and knowledge of neural networks, machine learning, and analytical modeling.

The Technische Universiteit Delft (TUD) is a world-class university ranked 20th in engineering and technology in the 2017 Times Higher Education World University Rankings. The current team at CAS consists of many students, post-docs and staff members. Creative and innovative research is the key object of the team.

To apply: email your CV, publication list, (links to) MSc and PhD reports to: t.g.r.m.vanleuken@tudelft.nl

More information: PRYSTINE, SunRISE, NewControl.

Contact

dr.ir. René van Leuken

Associate Professor

Circuits and Systems Group

Department of Microelectronics

Related project

Programmable Systems for Intelligence in Automobiles

(a) fail-operational sensor-fusion framework, (b) dependable embedded E/E architectures, (c) safety compliant integration of AI approaches for object recognition, scene understanding, and decision making