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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!
As the successful candidate, you will join the Security, Reasoning and Validation (SeRVal) group of the SnT, headed by Prof. Yves Le Traon. You will contribute more specifically to a partnership project with Creos S.A., the major energy network manager in Luxembourg. The topic of the project is “Secure, Reliable and Predictable Smart Grids” and it concerns the application of machine learning and simulation techniques to predict the impact of customers’ consumption on the Luxembourgish smart grid. The project involves (1) building decision making systems suggesting, based on consumption-related predictions, relevant actions to preserve the stability of the energy network, (2) designing methods to simulate worst-case scenarios (such as incidents) assessing the resilience of the grid to extreme conditions. The project is held in close collaboration with Creos S.A. and, as one of the successful candidates, you will be expected to program prototype tools showcasing the practical benefits of your research outcomes.
The supervision team you will be working with is:
You will be required to perform the following tasks:
Qualification: The candidate should possess an MSc degree (or equivalent) in Computer Science, or in Electrical Engineering with strong programming skills and good knowledge of computer science in general.
Experience: The ideal candidate should have some knowledge and/or experience in a number of the following topics:
Strong software development skills are mandatory.
Language Skills: Fluent written and verbal communication skills in English are required. French is a plus.
But wait, there’s more!
Students can take advantage of several opportunities for growth and career development, from free language classes to career resources and extracurricular activities.
Start date: between 1st of March and 1st of September 2021, upon agreement.
Job Reference: UOL03746
Application should include:
All qualified individuals are encouraged to apply.
Early application is highly encouraged, as the applications will be processed upon reception. Please apply ONLINE 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.
The University of Luxembourg aspires to be one of Europe’s most highly regarded universities with a distinctly international and interdisciplinary character. It fosters the cross-fertilisation of research and teaching, is relevant to its country, is known worldwide for its research and teaching in targeted areas, and is establishing itself as an innovative model for contemporary European Higher Education. It`s core asset is its well-connected world-class academic staff which will attract the most motivated, talented and creative students and young researchers who will learn to enjoy taking up challenges and develop into visionary thinkers able to shape society.
For further information, please contact us at maxime.cordy@uni.lu or yves.letraon@uni.lu.
MehrTitel | PhD Position in Machine Learning and Simulation-based Analysis for Smart Grids |
Employer | University of Luxembourg |
Job location | 6, rue Richard Coudenhove-Kalergi, L-1359 Luxembourg |
Veröffentlicht | November 18, 2020 |
Bewerbungsschluss | Offen |
Jobart | PhD/ Doktorand/in   |
Fachbereiche | Programmiersprachen,   Analysis,   Elektrotechnik,   Wissenschaftliches Rechnen,   Computergestützte Mathematik,   Computergestützte Ingenieurswissenschaft,   Maschinelles Lernen   |