Job Description:

In order to support the Flight Physics Capabilities Hamburg department , Airbus Operations is looking for a

Masterand in the field of Flight Physics Capabilities Hamburg (d/f/m)

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 Flight Physics Capabilities Hamburg department as a Masterand (d/f/m)!

Location: Hamburg

Start: As soon as possible

Duration: 6 months

Airbus @ Flight Physics Capabilities Hamburg

Airbus is a well known global leader in aeronautics, space and related services. The Flight Physics Capabilities team designs, develops, integrates and delivers all Flight Physics digital capabilities. It also innovates through using state of the art technologies and the application of the latest methods and tools. Within our mission we proactively support our customers inside and outside Flight Physics and bring value to the company through strengthened design processes.

You will be working at the largest production site for civil aircraft situated in Hamburg. Experience the special flair of Hamburg in your spare time where vibrant cosmopolitan culture meets nautic legacy.

Description of the Master Thesis

Surrogate models play a crucial role in multidisciplinary analysis and optimization, particularly in the early development of new aircrafts as well as for Loads In Service Support. These models, often based on Machine Learning (ML), offer significant advantages over traditional simulations by approximating the effects of parameter variations in real time. This capability enables substantial savings in time and costs while allowing for a broader exploration of parameter variations. As a result, optimal design points can be identified, and valuable insights into parameter interrelationships can be gained.

To train these surrogate models, high-quality datasets are essential. The Design of Experiments (DoE) methodology is employed to generate optimal datasets that map m simulation inputs to n simulation outputs. The overarching goal is to develop an adaptive DoE that can dynamically adjust based on specific needs - either to find the optimal design point or to perform active learning to enhance the performance of the surrogate model. By leveraging sequential simulations, it becomes possible to identify regions in the design space where additional data points provide the most information.

Focus of this Thesis

This thesis will focus on advancing the practical application of constrained DoE methodologies. Constrained DoE aims to reduce the design space, thereby simplifying the search process for optimization algorithms.

One existing constrained DoE framework is available, which transforms a cubical "base" DoE to comply with constraints in a post-processing step. However, this approach has limitations, including non-homogeneous sample distributions, which can introduce bias and require unnecessarily large sample sizes for ML applications.

Another more promising approach, currently being developed, is to describe the Design Space through the constraints upfront and only sample the admissible space. The primary objectives are to extend this new approach by incorporating additional input dimensions and parameter types like discreet and categorical parameters.

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.
• 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

• Familiarize yourself with the topic, previous theses and existing methods
• Familiarize yourself with the tools used
• Propose and evaluate methods for incorporating additional parameter types, such as discrete and categorical parameters, into the constrained DoE framework.
• Combine existing constrained DoEs into a comprehensive, high-dimensional DoE, adding missing dimensions
• Collaborate with flight physics engineers and domain experts to ensure that the implementation meets the specified requirements.

Desired skills and qualifications

• Currently pursuing a Master's degree in aeronautical or industrial engineering, IT or related discipline.
• Good communication and interpersonal skills
• Fluent in English (Working language, and expected language for thesis)
• Python programming
• Statistics
• Data engineering
• Familiarity with Design of Experiments (DoE) and/or surrogate modeling is a plus.

Please upload the following documents: cover letter, CV, relevant transcripts, enrolment 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:

Final-year Thesis

Experience Level:

Student

Job Family:

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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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