The University of Luxembourg is a multilingual, international research University.
The Interdisciplinary Centre for Security, Reliability and Trust (SnT) invites applications from highly motivated PhD candidates in the general area of software engineering within its SVV research group. SnT carries out interdisciplinary research in secure, reliable and trustworthy ICT systems and services, often in collaboration with industrial, governmental or international partners. For further information, you may refer to www.securityandtrust.lu
Doctoral candidate (PhD student) in Dynamic Adaptive Framework for IoT-enabled Disaster Management Systems(M/F)
The SVV research group is headed by Prof. Lionel Briand. The team focuses on the development and design of reliable, safe, and secure software systems, carrying out both upstream activities such as requirements quality assurance and architecture analysis, as well as downstream verification & validation activities, primarily software testing and analysis. For further information, you may refer to
This is a fully funded position for 3 years (extendable to an additional 4thyear) within a large, national research project (INSTRUCT - INtegrated Satellite-TeRrestrial Systems for Ubiquitous Beyond 5G CommunicaTions). The project is run in close cooperation with SES (https://www.ses.com/). Therefore, the objectives of the PhD project are defined in accordance to the project directions, and also aligned with the research interest of SES towards their next-generation of satellite systems.
The advent of communication technologies, e.g., 5G and software-defined network, underpins the era of internet of things (IoT), providing several opportunities to build intelligent systems. In the context of developing IoT systems, the PhD project aims to develop efficient and effective automated reconfiguration techniques to improve reliability of IoT systems. To achieve this goal, the successful candidate will develop a dynamic and self-adaptive control loop that monitors and controls IoT systems at runtime. The candidate will further rely on active machine learning techniques to predict congestions and delays in IoT networks beforehand and reconfigure the network to adapt to different situations in a more timely fashion.
The successful candidate will work with an academic supervisor from the University of Luxembourg and with an industrial supervisor from SES, and join a strong and motivated research team lead by Prof. Lionel Briand. The candidate is also expected to spend 25% of his/her time at the SES headquarters in Betzdorf, Luxembourg.
The position holder will be required to perform the following tasks:
Qualification: The candidate should possess a Master degree or equivalent in Computer Science, Software Engineering or Network Engineering.
Experience: The ideal candidate should have some knowledge and experience in a number of the following topics:
and be familiar with the principles of
The candidate must possess strong programming skills.
Language Skills: Fluent written and verbal communication skills in English are required.
The University offers a Ph.D. study program with an initial contract of 36 months, with a further possible 1-year extension if required. 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:
All qualified individuals are encouraged to apply.
Please apply ONLINE formally through the HR system. Applications by email will not be considered.
Early submission is encouraged; applications will be processed upon arrival.Mehr
|Titel||Doctoral candidate (PhD student) in Dynamic Adaptive Framework for IoT-enabled Disaster Management Systems|
|Employer||University of Luxembourg|
|Job location||6, rue Richard Coudenhove-Kalergi, L-1359 Luxembourg|
|Veröffentlicht||August 5, 2020|
|Jobart||PhD/ Doktorand/in  |
|Fachbereiche||Computer-Kommunikation (Netzwerke),   Informationssysteme (Unternehmensinformatik),   Programmiersprachen,   Softwareentwicklung,   Maschinelles Lernen  |