Data Science Internship Opportunity 2025 :-
Airbus is hiring candidates for the role of Data Scientist – Intern for the Bangalore, India locations. The complete details about Data Science Internship Opportunity 2025 are as follows.
| Company Name:- | Airbus India Private Limited |
| Required Education: | Graduation (M.Sc. / M.Eng. in Computer Science, Date Engineering, Mathematics, Aerospace) |
| Required Skills: | Strong Python skills. Statistics background Experience with Data Wrangling and Preprocessing. |
| Employment Type: | Permanent |
| Job ID: | JR10333719 |
Qualifications
- M.Sc. / M.Eng. in Computer Science, Date Engineering, Mathematics, Aerospace
- Strong Python skills.
- Statistics background
- Experience with Data Wrangling and Preprocessing.
- Experience in Design of Experiments for Data Generation.
- Proficiency in version control systems (e.g., Git) and software development best practices.
- Machine Learning & Deep Learning Model Development Cycle.
Roles & Responsibilities:
- Learn about the existing DoE libraries (OpenTurns, JohnDoE) by producing unit tests, docstrings, and documentation
- For the 1st DoE you switch from a temporary Fuel vector implementation to the official implementation
- Enhance the 1st DoE by increasing the complexity through an additional dimension: fuel density
- Enhance the 1st DoE by increasing the complexity through splitting dimension fuel_weight into: re_fuel_weight and de_fuel_weight
- Merge the 1st and 2nd DoE into one DoE
- add all missing independent further dimensions to reach practical applicability.
However, before investigating further we want to focus again more on the DoE. In order to decrease the design space, i.e. the space that optimization algorithms have to search through, a constraint DoE was developed. The aim of this work is to reach practical application of this constraint DoE by adding further input dimensions. A full description of an existing DoE to translate into a constraint DoE is available.
It works today with a cubical “base” DoE whose domain is transformed, in a post-processing step, to comply with the underlying constraints. The problem with this methodology though is that the final sample distribution is not homogeneous. This again leads to potential bias and unnecessary large sample sizes for ML applications.
Skills Required
- These surrogate models are (in our use cases) Machine Learning (ML) models. Accordingly, a data set must be available for the training of these surrogate models. The Design of Experiments (DoE) methodology is used to create an optimal data set for this purpose.
- The goal is to map ‘m’ simulation inputs to ‘n’ simulation outputs. The larger goal is to create an adaptive DoE which, based on the needs, either finds the optimum dataset or does active learning to increase the performance of the subsequently built surrogate model.
- Since the simulations are performed sequentially, it is possible to use the already calculated data points to determine the position in the design space where data points have the largest amount of information.
Data Science Internship Opportunity 2025 Application Process:-
Apply In Below Link
Note:– Only shortlisted candidates will receive the call letter for further rounds
