Multiscale and Machine Learning Modeling of Cold-Forming Processes in Steels

PhD position focused on the micromechanical simulation of cold-forming processes in steels and their implementation in a machine learning framework. The research will be carried out in collaboration with ArcelorMittal. The goal of the project is to develop computational models capable of predicting the mechanical behavior of the steel as a function of its cold-working processing parameters.

OTHER DETAILS

Ref. num. 2025-JS-R1-308
Multiscale and Machine Learning Modeling of Cold-Forming Processes in Steels

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.

Description

The groups on “Multiscale Materials Modeling” and “Computational Solid Mechanics” at IMDEA Materials are searching for candidates to fill a joint PhD position focused on the micromechanical simulation of cold-forming processes in steels and their implementation in a machine learning framework. The research will be carried out in collaboration with ArcelorMittal.

The goal of the project is to develop computational models capable of predicting the mechanical behavior of the steel as a function of its cold-working processing parameters. The work will involve:

Developing a micromechanics-based model to predict microstructure evolution and resulting properties of steel after cold deformation,

Implementing the modeling framework in home-made FFT or FEM simulation frameworks

Calibrating the micromechanical model, including uncertainty, using experimental mechanical data and microstructural data,

Develop a surrogate model of the polycrystalline micromechanical model, based on machine learning.

The PhD candidate will join a multidisciplinary team, interact closely with industrial partners, and gain experience in computational mechanics, computational materials science, micromechanics, and model reduction techniques.

Requirements

Candidates should hold BSc or MSc in Mechanical Engineering, Materials Science, Applied Mathematics or related disciplines with excellent academic credentials and a solid commitment to developing an academic career in an international and multidisciplinary environment.

Experience in programming (python, C++ or similar), finite elements modeling and numerical methods will be strongly valued.

Conditions

Starting date: open until filled.

Full time contract, including social security coverage.

Applications are processed upon reception. The position might be closed once ten working days have passed since publication, so we encourage early application.

The working language of the Institute is English. Full command of the English language is required in all positions.

Applications are processed upon reception. The position might be closed once ten working days have passed since publication, so we encourage early application.

The working language of the Institute is English. Full command of the English language is required in all positions.

WHAT YOU WILL FIND AT IMDEA:

Stimulating environment where you can grow professionally.

IMDEA Materials Institute is committed to equal opportunities, diversity and the promotion of a healthy work environment and work-life balance. Female applicants are encouraged to apply to our research and technical positions.

Besides on-the-job technical training, IMDEA Materials Institute is committed to training the Institute’s scientists and staff in “soft” or transversal skills