Research Associates (Post-Doc) positions in Performance Optimization Methodologies for MIMO Radar Networks

Online Application Only:http://emea3.mrted.ly/28f97

The University of Luxembourg is a multilingual, international research university.

The Interdisciplinary Centre for Security, Reliability and Trust (SnT) invites applications from PhD holders for conducting research in design and optimization methods for performance enhancement of next generation radar networks

SnT is carrying out interdisciplinary research in secure, reliable and trustworthy ICT systems and services, often in collaboration with industrial, governmental or international partners. The SIGCOM group in SnT is pursuing research on automotive radar applications in partnership with IEE (www.iee.lu), a Luxembourg based global leader in automotive safety sensing systems for occupant detection and classification. Recently, Prof. Bjorn Ottersten Director of SnT and head of SIGCOM, has been awarded the prestigious European Research Council (ERC) Advanced Grant to pursue research on cognitive radar systems with applications to automotive radar. For further information, you may visit www.securityandtrust.lu and http://wwwen.uni.lu/snt/research/sigcom.

Project Description

As novel applications emerge, the requirements on radar systems have grown significantly from being “a blip on the radar”, to providing an image like reconstruction of the surroundings. Currently, multi-static and widely-separated MIMO radars offer multi-view perspective. However, these systems suffer from the need for high rate synchronization, lack of performance guarantees, minimal exploitation of advances in waveform processing and machine learning among others. Thus, it is essential to go beyond the mature co-located MIMO and the current widely-separated MIMO radars towards achieving reliable imaging like performance for extended targets. In fact, many of these networks, such as in the automotive scenario, can involve large number of dynamic nodes. This necessitates devising novel radar-network architectures as well as exploring various optimization methodologies for waveform design, super-resolution parameter estimation and decentralized resource allocation.  

This emerging field opens interesting avenues for pursuing research in radar signal processing, especially on

• Relevant architectures for novel radar networks including information exchange mechanisms for identified use cases
• Associated signal processing elements including development of optimization algorithms for waveform, receiver design including high-resolution parameter estimation as well as their adaptation to dynamic scenarios
• System optimization using model based mechanisms as well as machine learning approaches; deep unfolding applications

The research associates will have further the opportunity to work towards in-lab demonstration of the developed techniques using USRP/SDR implementations.

The SIGCOM research group is in a unique position towards realizing the objectives of the project having exposure to radar signal processing through ongoing research projects, evolution of communication standards through participation and contribution as well as experience with prototype chip sets from the test-bench development activity.

Your Role

The successful candidates will join a strong and motivated research team lead by Prof. Björn Ottersten in order to carry out research in the area of signal processing for next generation radar systems.

The position holder will be required to perform the following tasks:

• Shaping research directions in line with project objectives, pursuing research and delivering project outputs
  • Carrying out cutting edge research activities in architectural definition, waveform design and receiver processing, enabling cognition in next generation radar networks
  • Participating in development and upgradation of the SDR based Radar test bench based on pursued research is considered as a plus
• Disseminating the results through scientific publications in high impact factor journals
• Presenting the results in the internationally well-known conferences and workshops
• Attracting funding in cooperation with partners
• Providing guidance to PhD and MSc students
• Assisting in teaching duties
• Organizing relevant workshops

For further information, please contact Bhavani.Shankar@uni.lu or Bjorn.Ottersten@uni.lu

Your Profile

Qualification: The candidate should possess (or be in the process of completing) a PhD degree or equivalent in Electrical/Electronics Engineering, Computer Science or Applied Mathematics.

Experience: The ideal candidate should have research project-based experience (FP7/H2020, Industry) and publication record in a number of the following topics:

• Optimization methodologies with application to Radar Systems
• Machine/Deep Learning with applications to Radar Systems
• Widely-separated MIMO Radar System, Waveform Design and Receiver processing
• Statistical Signal Processing

Exposure to USRP/SDR implementation and familiarity with FPGA programming is considered as an advantage. Development skills in one of the programming languages, MATLAB, LabVIEW or C++ are required.

Exposure to the latest radar technology and digital communications is desirable.

Language Skills: Fluent written and verbal communication skills in English are required.

We offer

The University offers a two-year employment contract that may be extended up to five years. The University is an equal opportunity employer. You will work in an exciting international environment and will have the opportunity to participate in the development of a newly created university.

Further Information

Application should be submitted online and include:

• Full CV, including list of publications and name (and email address, etc.) of three referees
• Transcript of all modules and results from university-level courses taken
• Research statement and topics of particular interest to the candidate (300 words)

Deadline for applications:  September 15, 2019.  Applications will be processed as they arrive; early application is highly encouraged.