Master Thesis Quantum enhanced Graph neural network

Job Description:

In order to support the Airbus Central Research & Technology Team, Airbus Defence and Space is looking for an

Intern (d/f/m) Quantum enhanced Graph neural network

Are you looking for an internship and want to get to know the work of an AI Researcher (d/f/m)?  Then apply now! We look forward to you supporting us in the Airbus Central Research & Technology Team as an intern (d/f/m)! 

Location: Ottobrunn
Start: as soon as possible
Duration: 6 months

1XRD is the Artificial Intelligence domain of Central Research & Technology composed of researchers and research engineers in artificial intelligence working to develop technologies bridging the gap between research and business. The team collaborates with academics and stirs research projects to push the state-of-the art towards solving Airbus relevant problems.

Your location
Our site is just a stone’s throw away from Munich, the beautiful capital of Bavaria. Are you into sports and other outdoor activities? The Alps and Lake Starnberg are within an hour’s reach, offering a multitude of recreational options.

Your benefits

• Attractive salary and work-life balance with a 35-hour week (flexitime).

• A final thesis is possible after consultation with 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

• Conduct research and literature reviews.

• Develop a classical Graph Neural Network algorithm and a Quantum enhanced Graph Neural Network algorithm.

• Benchmark the performances of the developed algorithms with current state of the art solutions.

• Compare the performance of the algorithms developed on the given use case.

• Present the work and results to the team.

Desired skills and qualifications

• Currently pursuing a master’s degree or PhD in a relevant field (e.g.,Computer Science,

• Mathematics, Physics, Quantum Computing and similar fields).

• Basic knowledge of machine learning and neural network algorithms. Experience in Graph Neural Networks is a plus.

• Basic understanding of Quantum Computing and Quantum Machine learning algorithms.

• Programming experience in Python. Pytorch is a plus.

• Basic experience in Qiskit.

• Fluent English language skills.

• Strong analytical and problem-solving skills.

• Excellent communication and teamwork abilities

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 Defence and Space GmbH

Employment Type:

Final-year Thesis

——-

Experience Level:

Student

Job Family:

Support to Management

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.