Airbus Internship 2025: Hiring Intern – Data Scientist

Airbus Internship 2025: hiring freshers for Intern – Data Scientist . Currently pursuing or recently completed a Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field are eligible to apply. The detailed eligibility and application process are given below.

Airbus Internship 2025 Details:

Company Website: www.airbus.com

Job Role: Intern – Data Scientist

Qualification: M.Sc. / M.Eng. in Computer Science, Data Engineering, Mathematics, Aerospace

Experience: Freshers

Batch: Recent Batches

Salary: ₹13 to 17 LPA (Expected)

Job Location: Bangalore

Last Date: ASAP

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Title: 

High-Dimensional Constrained Design of Experiments for ML applications

Introduction

Surrogate models are used in the area of multidisciplinary analysis and optimization. These surrogate models have the advantage over simulations that they can approximate the effects of parameter variations in real time. This enables savings in terms of time and costs when developing a new aircraft or aircraft variants. In addition, more variations of the parameters can be performed. The optimal point of design can be searched for and the necessary knowledge about the interrelationships of the parameters at the point of design is provided.

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.

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.

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Key Responsibilities

Today we have two constraint DoE’s:

1. zerofuel_mass, zerofuel_cg, fuel_weight

2. altitude, speed, vertical_loadfactor

• 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

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Qualifications

• 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

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Education

• M.Sc. / M.Eng. in Computer Science, Data Engineering, Mathematics, Aerospace

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How to Apply For Airbus Internship 2025?

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