Research Associate (Postdoc) on applied machine-learning and software engineering

University of Luxembourg, Luxembourg

Updated: over 2 years ago
Deadline: ;

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.

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!

The Interdisciplinary Centre for Security, Reliability and Trust (SnT) invites applications from PhD holders in the general areas of Engineering, Robotics and Computer Science. SnT is carrying 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 check: www.securityandtrust.lu .

As the successful candidate, you will join the Security, Reasoning and Validation (SeRVal) group of the SnT, under the supervision of Prof. Yves Le Traon and Dr. Maxime Cordy, working on a research collaboration between STATEC (the national statistical institute of Luxembourg) and SnT. STATEC is investigating to what extent data science and machine learning could contribute to improve their internal processes and statistical analyses. The special case of large-size datasets, typically arising from high frequency automated recording of human activites, and the way to extract its statistical essence, is of high importance the institute. Among various use cases, STATEC is particularly interested in conducting an in-depth analysis of fine-grained mobile phone data for various applications (e.g. impact of COVID-19 on behaviours and sanitary and economical impact, home-work commuting, mobility of cross-border workers or tourists).

You will help STATEC in capacity building in data science (DS)/machine learning for official statistics.


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