.Post-doctoral research associate - Artificial Intelligence applied to enhancement of X-ray Computed TomographyOTHER DETAILSRef.
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2025-FS-R2-215 Post-doctoral research associate - Artificial Intelligence applied to enhancement of X-ray Computed TomographyIMDEA 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.DescriptionThe candidate will participate in a public funded research project (METALIA – "Enabling technologies for the implementation of Artificial Intelligence in the value chain of the Additive Manufacturing of new metal alloys") aiming at developing materials for various applications (space, biomedical, aeronautic, etc.)
by additive manufacturing (AM).
This PhD/Post-doc focuses on enhancing the capabilities of XCT (X-ray Computed Tomography) technique by applying artificial intelligence to the reconstruction of the volumes, noise reduction and defect segmentation, classification and analysis.XCT is one of the most powerful techniques for defect assessments of additive manufacturing materials as it comprises a high resolution for the typical defect types in AM, a large volume of inspection which allows detecting thousands of defects at once, provides three-dimensional information of the inspected material and it is non-destructive.
Despite the advantages, XCT is a relatively slow, and therefore costly, technique due to the large number of projections (radiographs from different angle positions) required to provide a high-quality volume reconstruction.
Additionally, when measuring materials with high X-ray absorption such as metals, noise and artifacts are generated in the images.
Common data limitations during XCT experiments include: i) acquiring only a limited number of projections; ii) noise in the projection images; and iii) acquiring projections for a limited angular range.
Thus, the main activity of the candidate within the project will correspond to data analysis, and the development and implementation of AI-based tools to enhance and accelerate XCT pre- and post-processing.
Solutions will be implemented in both, laboratory and synchrotron XCT data.The candidate is expected to have a solid background in programming, artificial intelligence and data processing