PhD – Innovative maintenance - 10462056 - (m/f)

Job description

Airbus Operations SAS

Airbus is a global leader in aeronautics, space and related services. In 2018 it generated revenues of € 64 billion and employed a workforce of around 134,000. Airbus offers the most comprehensive range of passenger airliners. Airbus is also a European leader providing tanker, combat, transport and mission aircraft, as well as one of the world's leading space companies. In helicopters, Airbus provides the most efficient civil and military rotorcraft solutions worldwide.

Our people work with passion and determination to make the world a more connected, safer and smarter place. Taking pride in our work, we draw on each other's expertise and experience to achieve excellence. Our diversity and teamwork culture propel us to accomplish the extraordinary - on the ground, in the sky and in space.

Job Description

Contact :
You can apply online on our career site (https://www.airbus.com/careers.html) by attaching your CV and cover letter to this job offer.
By submitting your CV or application, you consent to Airbus using and storing your information for the purpose of tracking your application or future employment. This information will only be used by Airbus

Description of the job :
Commercial aircrafts are equipped with large arrays of sensors that monitor the status of their subsystems during flight. The resulting data is collected and centralized after each flight, yielding very large databases of recordings.

One area where these recordings are sought to be exploited is to determine technical issues or performance losses in innovative ways. This would enable more accurate view on maintenance frequency.

At Airbus, the flight physics department deals with the design of the aircraft from an overall perspective. During the design phase, it is responsible for:
- the shape of the aircraft,
- the performance
- loads calculations and many different phenomena relating to the interaction between the airframe, its payload and its aerodynamics.
During the operations phase, the department produces:
- a theoretical model of the aircraft or digital twin,
- improves it using flight data,
- and uses it to support the fleet of flying aircrafts by improving its performance and reliability. This is made possible by collecting and analysing data from in-service aircrafts.

Task & accountabilities :
This PhD thesis proposal aims, in this respect:
- to investigate machine learning and artificial intelligence, including deep recurrent neural networks
- to build a model for some specific use case such as airframe vibrations or performance degradation.
Research objectives include formalizing industrial operations such a scheduled maintenance or variability in aircraft operations in sequence-oriented or time-series oriented machine learning techniques. Then to provide a general framework for a new model to be applied mainly to flight physics.

Required skills :
Very solid knowledge in Industrial engineering as well as data science and applied mathematics
Advanced understanding in a/c maintenance/performance as well as AI and machine Learning.
Advanced level in Matlab and Python
The following soft skills are primordial to ensure a good experience and that the mission be a success:
- Rigor
- Curiosity, creativity
- Self driven
- Good interpersonal and communication skills
- Autonomy/proactivity
- Analysis and synthesis skills
- Advanced level of French and advanced level of English

This job requires an awareness of any potential compliance risks and a commitment to act with integrity, as the foundation for the Company’s success, reputation and sustainable growth.

By submitting your CV or application you are consenting to Airbus using and storing information about you for monitoring purposes relating to your application or future employment. This information will only be used by Airbus.
Airbus is committed to achieving workforce diversity and creating an inclusive working environment. We welcome all applications irrespective of social and cultural background, age, gender, disability, sexual orientation or religious belief.