Surrogate models help reduce time and cost in aircraft design by quickly approximating simulation results. These models use machine learning and need good datasets to learn from. To create such datasets, a method called Design of Experiments is used. It selects input values smartly to cover the design space well. A special type called constraint DoE reduces the search area by following specific rules. But the current method leads to uneven data points, causing bias and larger datasets than needed. The goal now is to improve this by adding more input dimensions and making the data more balanced for better ML performance.
Job Overview:
| Field | Details |
| Job Title | Data Scientist – Intern |
| Location | Bangalore Area |
| Education | M.Sc. / M.Eng. in Computer Science, Data Engineering, Mathematics, Aerospace |
| Compliance Note | Requires awareness of compliance risks and commitment to integrity |
| Time Type | Full Time |
| Job Requisition ID | JR10333719 |
Responsibilities:
- Understand and test existing DoE libraries like OpenTurns and JohnDoE.
- Improve current DoE implementations by:
- Switching to official fuel vector implementation.
- Adding complexity with new dimensions like fuel density.
- Splitting existing variables.
- Merge existing DoEs into a unified model.
- Extend the DoE with more input dimensions for real-world applicability.
Required Skills:
- Strong proficiency in Python.
- Good understanding of Statistics.
- Hands-on experience with Data Wrangling and Preprocessing.
- Knowledge of Design of Experiments (DoE) for data generation.
- Familiarity with version control systems like Git.
- Experience in Machine Learning & Deep Learning model development lifecycle.
Preferred:
- Experience with DoE libraries like OpenTurns, JohnDoE.
- Ability to handle high-dimensional data and implement constraint-based models.
- Familiarity with aerospace-related datasets or simulations.
About the Company:
Airbus Innovation Centre β India & South Asia, part of a global Airbus innovation network, focuses on building cutting-edge technologies in areas like:
- Artificial Intelligence
- Unmanned Air Systems
- Connectivity
- Space Tech
- Decarbonization
- Digital Engineering
The centre collaborates with top universities, startups, labs, and internal Airbus engineering teams to drive innovation.
Qualification:
- Strong academic foundation in science/engineering disciplines relevant to ML and simulations.
- Awareness of compliance risks and a strong ethical commitment in work.
