The University of Luxembourg is a multilingual, international research University.
The University of Luxembourg is looking for its Interdisciplinary Centre of Security and Trust (SNT) for a :
Doctoral candidate (PhD student Position) “Optimization and Machine Learning for 5G Networks”,
- Ref: 50013711 – (R-AGR-3283-10-C)
- Fixed Term Contract up to 3 years in total, pending satisfaction of progress milestones (CDD), on full time basis (40hrs/week).
- Number of positions: 1
- Employee and student status
The Interdisciplinary Centre for Security, Reliability and Trust (SnT) invites applications from PhD candidates in the general area of signal processing for wireless communications. 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 Centre is rapidly expanding its research activities and is seeking highly motivated PhD candidates who wish to pursue research in close cooperation with our partners. For further information you may check: www.securityandtrust.lu and http://wwwen.uni.lu/snt/research/sigcom .
The successful candidate will be supported by the European Research Council (ERC) project “Actively Enhanced Cognition based Framework for Design of Complex Systems (AGNOSTIC)”. The candidate will join an internationally leading and motivated research team, under the supervision by Prof. Björn Ottersten (IEEE Fellow, SnT, Luxembourg) and Dr. Symeon Chatzinotas (IEEE Senior Member, SnT, Luxembourg) in order to carry out research to pursue a PhD on the following topics:
- Advanced optimization and machine learning applied to 5G wireless networks and Internet of Things (IoT)
- Machine learning for IoT security with fault detection/trustfulness of IoT devices, congestion detection
- Mathematical modeling and algorithm development to improve 5G-IoT network performance
- Resource management in 5G network slicing
The position holder will be required to perform the following tasks:
- Carrying out research in the predefined areas
- Disseminating results through scientific publications
- Participating in drafting project deliverables
- Presenting the results in the well-known international conferences and workshops
For further questions, please contact us at email@example.com, Thang.Vu@uni.lu or Symeon.Chatzinotas@uni.lu. For institution information, you may check: www.securityandtrust.lu, http://wwwen.uni.lu/snt/research/sigcom.
Qualification: The candidate should possess an MSc degree or equivalent in Electronic Engineering, Computer Science or Applied Mathematics.
The ideal candidate should have some knowledge and experience in a number of the following topics:
- Signal processing for wireless communications
- Radio resource optimization in 4G/5G/IoT networks
- Applications of machine/deep learning in wireless communications and IoT networks
and be familiar with the principles of
- Optimization theory
- Machine/deep learning
- Statistical signal processing
Programming skills: familiar with optimization/deep learning tools, and strong in one of the programming languages MATLAB, Python, or C++.
- Language Skills: Fluent written and verbal communication skills in English are mandatory
The University offers a Ph.D. study program with a Fixed Term Contract up to 3 years in total, pending satisfaction of progress milestones (CDD), on full time basis (40hrs/week). The University offers highly competitive salaries and 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.
Application should include:
- Full CV, including list of publications and name (and email address, etc.) of 2-3 referees
- 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 online. Deadline for applications: 31 August, 2019
Please apply formally through the HR system. Applications by email will not be considered.
Early application is highly encouraged as the applications are processed upon reception.
Link : http://emea3.mrted.ly/26op9