Source:
Publication date: 30/08/2026
The design of aerospace structures is a complex and multidisciplinary process, integrating various fields of expertise such as mechanical engineering, materials, aerodynamics, and manufacturing systems. One of the fundamentals of this process is ensuring structural integrity under all operating conditions and possible scenarios. In this context, structural analysis faces multiple sources of uncertainty, including geometric and dimensional tolerances, material properties, and loading conditions. To overcome these limitations, the use of machine learning models is proposed to efficiently analyze and predict reserve factors (RF). The method will enable precise analyses with a reasonable computational cost, while consistently meeting the high safety standards required by the EASA (European Union Aviation Safety Agency) certification.
Responsibilities
Use machine learning models to efficiently analyze and predict reserve factors (RF); Ensure structural integrity under all operating conditions and possible scenarios; Perform structural analysis considering geometric and dimensional tolerances, material properties, and loading conditions; Meet EASA certification safety standards through precise analyses.Requirements
Educated to degree level (or equivalent) in Aerospace Engineering; Structural composite stress analysis; Mathematical mindset; Analytical and problem-solving skills; Programming languages (C++/Fortran/Python); Final Element modelling techniques (Abaqus, Nastran, Hypermesh); Language Skills: Negotiation level of English, Spanish; Team spirit and great collaboration skills; Ability to work in complex and international environments.Benefits
Attractive study grant; Opportunity to study a Master organized in three training blocks; Professional development with strong academic background and in-depth collaboration in Airbus projects; Expand your network within the aeronautical industry; Be part of diversity and teamwork culture; Customized development solutions.How to apply
Apply online: https://www.jobs.pl/en/oferta/100022764/aplikuj
JOBS.pl · https://www.jobs.pl/en/discover-ii-2026-2027-internship-artificial-intelligence-for-composite-structural-sizing-process-oferta-100022764


















