IMDEA Materials Institute is a public research organization founded in 2007 by Madrid’s regional government to carry out research of excellence in Material Science and Engineering by attracting talent from all over the world to work in an international and multidisciplinary environment. IMDEA Materials has grown rapidly since its foundation and currently includes more than 120 researchers from 22 nationalities and has become one of the leading research centers in materials in Europe which has received the María de Maeztu seal of excellence from the Spanish government. The research activities have been focused on the areas of materials for transport, energy, and health care and the Institute has state-of-the-art facilities for processing, characterization and simulation of advanced materials.

More information can be found at our webpage.

The work involves composite material manufacturing, material testing, ultrasonic inspection, X-ray characterization, data visualization and analysis (programming required), as well as digitalization activities. Basic programming knowledge (preferably in Python) is desirable, together with experience in data visualization and image analysis.

The main objective of the position is the manufacturing of composite materials using hot-press processing techniques (and potentially other manufacturing methods depending on the project progress) under controlled processing conditions to generate controlled defect populations. These materials will subsequently be characterized using ultrasonic testing and X-ray Computed Tomography (XCT).

In detail, the main research tasks include:

• Manufacturing of composite material samples with controlled processing parameters (with support from IMDEA technical staff)
• Collection and analysis of ultrasonic (US) and XCT data (with support from IMDEA technical staff)
• Identification and characterization of defects and microstructural features in composite materials
• Implementation of data fusion techniques combining US and XCT information
• Assist to development of AI-based models for defect and microstructure identification from multimodal inspection data

Mandatory Requirements

It is important that the candidate meets the requirements indicated in the call:

1. Age (16–29 years old)
2. Degree/qualification (according to the requested profile)
3. Registered as an unemployed job seeker at the emplyment office within the National Employment System (SEPE) before hiring: https://sede.comunidad.madrid/...
4. No employment relationship with IMEDEA (during the 6 months prior to hiring).
   
   The position is supported by Young Researchers Programme of the Community of Madrid within the framework of the ESF+ Programme: Strategic Technologies for Europe Platform (STEP) / Programa de Jóvenes Investigadores de la Comunidad de Madrid en el marco del Programa FSE+: Plataforma de Tecnologías Estratégicas para Europa (STEP)

Other requirements

• Recent Master’s graduates (or candidates close to graduation) in Materials Science and Engineering, Mechanical Engineering, Industrial Engineering, Physics, or related disciplines, with strong academic credentials and interest in advanced manufacturing, non-destructive evaluation, and digitalization technologies.
• Previous experience or knowledge in composite materials, X-ray Computed Tomography (XCT), ultrasonic inspection, image analysis, data processing, or AI-based methodologies will be considered an advantage. As the work will involve close interaction with industrial stakeholders and multidisciplinary teams, strong teamwork and communication skills are essential.
• Basic programming knowledge in any language is desirable, preferably Python for compatibility with existing workflows and software developments.
• Full proficiency in English, both written and spoken, is mandatory.
• Interested candidates should submit their Curriculum Vitae, a brief cover letter describing their motivation and interests, and academic transcripts/credentials.

• Full-time contract for 12 months.
• The post will remain active and open until filled.