University of Luxembourg

PhD candidate in Machine Learning for Digital Twin in Aerospace Industry

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The University of Luxembourg seeks to hire outstanding researchers at its Interdisciplinary Centre for Security, Reliability and Trust(SnT), in the SERVAL team under Prof. Le Traon (http://wwwfr.uni.lu/snt/research/serval). SnT is carrying out interdisciplinary research in secure, reliable and trustworthy ICT (Information and Communication Technologies) systems and services, often in collaboration with industrial, governmental or international partners.  SnT is active in several international research projects funded by national, European and international research programmes (e.g., FNR, Horizon Europe). For further information you may check: https://www.uni.lu/snt

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 aerospace industry is committed to improving the vehicle’s life cycle management due to the long vehicle life cycle (more than 40 years including production, manufacturing and in service). Digital Twin is recognised (e.g., by Boeing, Airbus, GE Company, NASA, EASA) to have the potential to enable effective life cycle management tools, by providing an iterative closed-loop process that integrates all different stages of air vehicles, from design, manufacturing to operation and maintenance (O&M) until their end of life. Although aerospace industry is data-rich, the data is currently not connected to Digital Twin platforms, and thus optimal load monitoring and effective fault diagnosis cannot be achieved. To solve part of this challenge, the AVATAR project, standing for “Digital Twin for Transformative Air Vehicle with IoT sensors Towards Safer Skies”, has been granted by the European Commission (under the Horizon Europe programme) to investigate and develop a new Digital Twin platform. This platform will consist of a new “smart skin” (fitted with a high density of sensors of different types) designed and manufactured by the Danish Technological Institute (Denmark), which will then be deployed, tested and validated through wind tunnel testing and by real flight tests of EVEKTOR’s ultralight Cobra airplane (Czech Republic) and Nordic Wing’s Astero electric unmanned aircraft (Denmark).

Your Role

In this context, the PhD candidate will be in charge of investigating innovative Machine Learning (ML)-based solutions and methodologies to predict the Remaining Useful Life (RUL) of the aircraft structure [Ra19] using, among other data sources, the data generated by the “smart skin” during the operational phase, and communicate the estimations to the end-user in the form of actionable maintenance indications. Several scientific challenges will be addressed in this PhD, spanning from the necessity to efficiently and optimally cope with (i) imbalanced data [DA22] by e.g. generating synthetic data using GAN/transformers; (ii) possible model drifts over time [Lu18] by e.g. developing ‘vigilance’ mechanisms to detect them and trigger appropriate actions (e.g., rising alerts to operators, model re-training, etc.); or still to cope with (iii) the lack of transparency and explainability of existing ML techniques [Sa19] by e.g. implementing and comparing existing eXplainable AI (XAI) frameworks when dealing with aircraft PdM and time series, and, if needed, to adapt them to our problem.

Overall, this PhD will be enrolled in a very exciting, collaborative and international project granted under the Horizon Europe programme (see the call at: https://ec.europa.eu/info/funding-tenders/opportunities/portal/screen/opportunities/topic-details/horizon-cl5-2022-d5-01-13), giving the opportunity for the candidate to meet and spend some times with the different partners from the consortium such as Imperial College London (ranked as the 7th best university in the world), EVEKTOR and Nordic Wing (aircraft manufacturers) respectively located in Czech Republic and Denmark.

Your Profile

The expected PhD candidate should have:

  • A Computer Science background
  • Good expertise in Machine Learning, Data Science
  • Good programming skills (python, Java…)
  • Fluent written and verbal communication skills in English are mandatory
  • Commitment, team working and a critical mind

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 45 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, health insurance and subsidised living and eating
  • 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

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.

In Short

  • Contract Type: Befristeter Vertrag 36 Monat
  • Work Hours: Full Time 40.0 Stunden pro Woche
  • Location: Kirchberg
  • Internal Title: Doctoral Researcher
  • Job Reference: UOL05201

The yearly gross salary for every PhD at the UL is EUR 38028 full time

How to apply

Applications should be submitted online and include:

  • Curriculum Vitae (including your contact address, work experience, publications)
  • Cover letter indicating your motivation and expected starting date (could depend on your ongoing formation)
  • Contact information for 1 or 2 referees (incl., email + phone of your contacts)

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.

For inquiries please contact

Sylvain KUBLER - sylvain.kubler@uni.lu

Maxime CORDY - maxime.cordy@uni.lu

DESCRIPCIÓN DEL PUESTO

Título
PhD candidate in Machine Learning for Digital Twin in Aerospace Industry
Ubicación
2, avenue de I'Universite Belvaux, Luxemburgo
Publicado
2022-08-15
Fecha límite de aplicación
Unspecified
Tipo de trabajo
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