SnT is a leading international research and innovation centre in secure, reliable and trustworthy ICT systems and services. We play an instrumental role in Luxembourg by fueling innovation through research partnerships with industry, boosting R&D investments leading to economic growth, and attracting highly qualified talent.
The SPARC group in SnT is pursuing research on the signal processing applications in radar and communications systems in partnership with national and international academic and industrial collaborators. Updated information about the recent activities of SPARC can be found here: www.radarmimo.com/
The group has been working on waveform design and signal processing for next generation of mmWave radar sensors in different applications, including automotive, indoor sensing, in-cabin monitoring, occupancy sensing, drones, multi-copters, gesture recognition, smart buildings, smart street lighting, smart factories, healthcare, and robotics. The emerging radar sensors that are in use by the group, are using multiple input multiple output (MIMO) technology and can be connected to build a distributed and co-operative network.
To develop the signal processing techniques, the group researchers are using real data captured by the available radar modules at the lab, or the custom-built MIMO radar with flexibility of changing the transmit waveform and signal processing units on the fly. Its members have been involved in several seminal papers in waveform optimization, vital signs monitoring, joint radar and communications, sparse arrays, interference modeling, 4D imaging radars, distributed sensing, and tracking. SPARC has also collaborated with long term industry partner IEE S.A (www.iee.lu), University of Naples Federico II, US Airforce Laboratories, TU Berlin, and TU Munich, among others. Personnel at SPARC have also been involved in European Research Council (ERC) Advanced Grant and in ERC Proof-of-Concept activities.
We’re looking for people driven by excellence, excited about innovation, and looking to make a difference. If this sounds like you, you’ve come to the right place!
To augment the current research and investigate interesting novel problems identified in the areas mentioned below, the SPARC group is looking for two Doctoral candidates to work on the following topics:
PhD Topic 1: “Optimizing Signal Processing Algorithms in Dynamic Environments through Learning”
This research topic delves into the functional aspect of cognitive radar systems. By implementing a learning strategy, the project aims to establish a functional framework for a metacognitive radar. Specifically, the focus lies in leveraging deep learning techniques to learn and select the most optimal algorithms for tasks such as waveform design, receiver configuration, and detection methods. The goal is to enhance radar performance in dynamic environments through intelligent algorithm selection.
PhD Topic 2: “Enhancing Data Processing Techniques for Dynamic Environments through Learning”
This research topic centers around the executive level of cognitive radar systems. The project endeavors to construct a functional framework for a metacognitive radar by employing a learning strategy. The primary objective is to develop proficiency in selecting the most effective algorithms for data processing tasks like segmentation, clustering, data association, and tracking, using a deep learning framework. The aim is to enhance radar capabilities in dynamic environments by leveraging intelligent algorithmic decision-making.
The candidate should possess (or be in the process of completing) a MSc degree or equivalent in Electrical/Electronic Engineering, Computer Science or Applied Mathematics or Physics with electromagnetic background.
Experience: The ideal candidate should have good theoretical background in some of the following topics:
- Radar Systems and Signal Processing
- Statistical Signal Processing
- Optimization methodologies
- Machine Learning and deep learning
Development skills in MATLAB/python/C++ is required and exposure to the latest signal processing techniques, linear algebra and deep learning are desirable. Exposure to HW prototyping using USRPs is a plus.
Language Skills: Fluent written and verbal communication skills in English are required.
Here’s what awaits you at SnT
- A stimulating learning environment. Here post-docs and professors outnumber PhD students. That translates into access and close collaborations with some of the brightest ICT researchers, giving you solid guidance
- Exciting infrastructures and unique labs. At SnT’s two campuses, our researchers can take a walk on the moon at the LunaLab, build a nanosatellite, or help make autonomous vehicles even better
- The right place for IMPACT. SnT researchers engage in demand-driven projects. Through our Partnership Programme, we work on projects with more than 55 industry partners
- Multiple funding sources for your ideas. The University supports researchers to acquire funding from national, European and private sources
- Competitive salary package. The University offers a 12 month-salary package, over six weeks of paid time off, meal vouchers and health insurance
- Be part of a multicultural family. At SnT we have more than 60 nationalities. Throughout the year, we organise team-building events, networking activities and more
- Boost your career. Students can take advantage of several opportunities for growth and career development, from free language classes to career resources and extracurricular activities
But wait, there’s more!
- Contract Type: Fixed Term Contract 36 Month (extendable up to 48 months if required)
- Work Hours: Full Time 40.0 Hours per Week
- Employee and student status
- Location: Kirchberg
- Job Reference: UOL05813
The yearly gross salary for every PhD at the UL is EUR 39953 (full time)
How to apply
Applications should be submitted online and include:
- Full CV, including list of publications, bachelor/master thesis and names (and contact information including email addresses) of references
- Transcript of all modules and results from university-level courses taken
- Research statement and topics of particular interest to the candidate (300 words)
All qualified individuals are encouraged to apply.
Early application is highly encouraged, as the applications will be processed upon reception. Please apply formally through the HR system. Applications by email will not be considered.
The University of Luxembourg embraces inclusion and diversity as key values. We are fully committed to removing any discriminatory barrier related to gender, and not only, in recruitment and career progression of our staff.
About the University of Luxembourg
University of Luxembourg is an international research university with a distinctly multilingual and interdisciplinary character. The University was founded in 2003 and counts more than 6,700 students and more than 2,000 employees from around the world. The University’s faculties and interdisciplinary centres focus on research in the areas of Computer Science and ICT Security, Materials Science, European and International Law, Finance and Financial Innovation, Education, Contemporary and Digital History. In addition, the University focuses on cross-disciplinary research in the areas of Data Modelling and Simulation as well as Health and System Biomedicine. Times Higher Education ranks the University of Luxembourg #3 worldwide for its “international outlook,” #20 in the Young University Ranking 2021 and among the top 250 universities worldwide.
Radars are becoming utilitarian with multiple applications in daily life. As the applications increase, so does the sophistication of the radar system. In this context, the incorporation of cognition into radar systems has revolutionized the field of radar technology. This advancement has brought forth a new era of radar system design and engineering, captivating the attention of researchers and industry professionals worldwide. Cognitive radars offer advanced sensing capabilities by quickly optimizing both transmit and receive processing in response to the changes in the target environment. These systems reveal unique opportunities in sensing by encouraging greater control of transmitters and higher adaptability of receivers than their non-cognitive counterparts.
In general, a cognitive radar is designed to apply a single specific framework or algorithm to achieve its desired performance based on a pre-determined criterion. Since radars perform a variety of tasks such as detection, estimation, and tracking, in practice, a single cognitive radar framework is insufficient to address changes in the system hardware and channel environment over long periods of time. Further, as the complexity of radar system increases (e.g., use of multiple antennas and waveforms, dense systems, multiple extended targets) and the channel conditions changes (low signal-to-noise-ratio/SNR and presence of clutter), a single cognitive algorithm is unable to address the changing performance requirements. Thus, conventional cognitive radars face several challenges in not only making intelligent abstraction of received data in real-time but also adapting sensing techniques to a highly dynamic and complex environment.
To address challenges beyond the conventional cognitive radars, there has been recent interest in enhancing, enabling, and engineering novel processing methods to achieve higher and complex levels of cognition. In such cases, a strategy to combine various cognitive frameworks in a metacognitive radar is highly desirable. This project is aimed at exploring metacognitive aspects in the development of next generation intelligence abilities in radar engineering. The project concentrates on the development of learning-based methods to enable metacognition. As a result, not only will the best solution be found through environmental feedback, but also the best method will be chosen. In this context, a learning procedure is required to find the optimum objective that satisfies all the dynamic scene constraints among the many optimization objectives available for waveform design, receiver design, and data processing. The focus of the project is on moving beyond the existing and evolving research in literature on cognitive radars and exploring aspects of metacognition. This involves enhancing the ability of the sensing system “to learn to learn”. Hence, it involves the action of sensing, the cognition of the sensed information and the process of learning from cognition. The resulting applications can be in all fields of sensing, including automotive, healthcare monitoring, safety, and security, among others.