About Airbus Innovation Centre – India & South Asia
At Airbus Innovation Centre, we pioneer disruptive technologies that shape the future of aerospace. Collaborating with global partners, big tech enterprises, startups, research institutions, and universities, we focus on domains such as AI, digital engineering, industrial automation, space tech, unmanned air systems, and decarbonization technologies.
As one of three global Airbus Innovation Centres, our centre in India has a strong focus on AI and Digital Engineering. We develop innovative solutions from the ground up, contributing to operational excellence and Airbus’ long-term Innovation & Technology roadmap.
| Company Name: | Airbus |
| Job Role: | Data Scientist – Intern |
| Experience: | Freshers |
| Location: | Bangalore, India |
| CTC/Salary: | INR 30K-60K/Month (Expected) |
Position Title:
High-Dimensional Constrained Design of Experiments (DoE) for Machine Learning Applications
Introduction
In the field of multidisciplinary analysis and optimization, surrogate models—particularly machine learning (ML) models—offer the ability to approximate complex simulation behaviors in real-time, significantly reducing time and cost in aircraft design. However, creating optimal datasets for training these models is crucial.
Our focus is to enhance the Design of Experiments (DoE) methodology to:
- Map ‘m’ simulation inputs to ‘n’ outputs.
- Develop an adaptive DoE capable of optimizing dataset quality or actively learning to boost ML performance.
- Reduce design space complexity while maintaining sample uniformity.
The challenge lies in evolving a constraint DoE approach. Current cubical base DoE methods often result in non-homogeneous sample distributions, leading to bias and inefficiencies. Our aim is to implement a multi-dimensional, constraint-based DoE for practical, scalable ML applications in aerospace.
Key Responsibilities
- Understand and document existing DoE libraries (OpenTurns, JohnDoE).
- Produce comprehensive unit tests, docstrings, and user documentation.
- Transition the fuel vector implementation in the first DoE to the official standard.
- Enhance the first DoE by:
- Adding fuel density as a new dimension.
- Splitting fuel_weight into re_fuel_weight and de_fuel_weight.
- Merge the two existing DoEs:
- zerofuel_mass, zerofuel_cg, fuel_weight
- altitude, speed, vertical_loadfactor
- Expand the DoE with additional independent dimensions for real-world applications.
Required Qualifications
- Education: M.Sc. / M.Eng. in Computer Science, Data Engineering, Mathematics, Aerospace Engineering, or a related field.
- Strong proficiency in Python and familiarity with statistical methods.
- Experience in:
- Data wrangling and preprocessing.
- Design of Experiments (DoE) for data generation.
- Version control (Git) and best practices in software development.
- Machine Learning & Deep Learning model development cycles.
- Ability to work collaboratively in cross-functional, innovative environments.
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Why Join Us?
- Work at the forefront of AI and Digital Engineering in aerospace.
- Collaborate with a global ecosystem of experts, startups, and strategic partners.
- Drive innovation in ML-driven optimization for real-world aerospace challenges.
- Develop your skills in a dynamic, future-focused environment.