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Master student in the field of data science/AI (d/f/m), title of thesis “Towards certifiable large language models and information extraction based on machine learning for the aircraft industry”

Airbus Group

Job Description:

In order to support the Data-Driven Services & Maintenance department, Airbus Operations is looking for a

Master student in the field of data science/AI (d/f/m), title of thesis “Towards certifiable large language models and information extraction based on machine learning for the aircraft industry”

You are looking for a master thesis and want to get to know the work of a job title? Then apply now! We look forward to you supporting us in the Data-Driven Services & Maintenance department as a master student (d/f/m)!

  • Location: Hamburg
  • Start: 01.04.2024
  • Duration: 6 months

Want to spread your wings? What if YOUR ADVENTURE begins with US? We offer you the opportunity to work in a world leader company in its field, focused on digital technology, at the forefront of research and innovation.

A master thesis (d/f/m) position offer with the working title "Towards certifiable large language models and information extraction based on machine learning for the aircraft industry" has just been opened within Airbus Operations GmbH.

You will join our Data-Driven Services & Maintenance department consisting of more than 80 people in several Engineering Centers in Europe and in India. Its mission is to deliver “world-class” Products, Services & Support and spread excellence for Maintenance, Certification & Artificial Intelligence, keeping our Customers in mind, our people at heart and innovation in our DNA.

This thesis position will consist of studying and implementing techniques for speech recognition, natural language processing and information extraction based on deep/machine learning tools and ontologies. The application can concern aircraft systems and will use in-service data to evaluate the performance of the proposed methods. This involves studying state of the art speech and pattern recognition, baseline language models and autoregressive algorithms, defining relevant performance metrics for the business and addressing the issues of managing the uncertainty of results (characterization and quantification) to develop a robust methodology.

Your location

You will be working at the largest production site for civil aircraft situated in Hamburg. Its location on the southern banks of the river Elbe includes the option to commute by ferry. Experience the special flair of Hamburg in your spare time where vibrant cosmopolitan culture meets nautic legacy.

Your benefits

  • Work-life balance with a 35-hour week (flexitime).
  • Mobile working after agreement with the department.
  • Traveling overseas or within Germany (team events) is possible after consultation and agreement from the department.
  • International environment with the opportunity to network globally.
  • Work with modern/diversified technologies.
  • At Airbus, we see you as a valuable team member and you are not hired to brew coffee, instead you are in close contact with the interfaces and are part of our weekly team meetings.
  • Opportunity to participate in the Generation Airbus Community to expand your own network.

Your tasks and responsibilities

You will be under the responsibility of a supervisor who will help you to identify your professional objectives and support you in the development of your skills.

You will work on to the following thesis elements:

  • Bibliographic review of methods and techniques for speech recognition and natural language processing based on machine learning.
  • Comparative review and implementation of models.
  • Characterization of the various performances and risks (e.g. certifiability) associated with the use cases studied.
  • Definition of relevant metrics and indicators to estimate robustness during in-service operation.
  • Documentation of down-selected solution.

Desired skills and qualifications

  • You are currently registered as a full time Master degree student (d/f/m) in the field of Data Science, Statistics, (applied) Mathematics or equivalent.
  • First experience in Data Science, Machine Learning, Deep Learning or Statistics/ Probability studies.
  • Experience within Certifiable AI, Generative AI, Natural Language Processing (NLP), Large Language Models, Automatic Speech Recognition or Reinforcement Learning from human feedback (RLHF) are an asset.
  • Mastering tools in Python (numpy, tensorflow, pytorch, jax, …).
  • Interest in innovation and research.
  • You have good communications skills and an advanced level in English.

Please upload the following documents: cover letter, CV, relevant transcripts, enrollment certificate. 

Not a 100% match? No worries! Airbus supports your personal growth.

Take your career to a new level and apply online now!

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.

Company:

Airbus Operations GmbH

Employment Type:

Internship





Experience Level:

Student

Job Family:

Digital <JF-IM-DI>

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.

Airbus is, and always has been, committed to equal opportunities for all. As such, we will never ask for any type of monetary exchange in the frame of a recruitment process. Any impersonation of Airbus to do so should be reported to emsom@airbus.com.

At Airbus, we support you to work, connect and collaborate more easily and flexibly. Wherever possible, we foster flexible working arrangements to stimulate innovative thinking.

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Confirmed 17 hours ago. Posted 30+ days ago.

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