PhD in Machine Learning for Stainless Steel Constitutive Modeling at University of Groningen

University: University of GroningenCountry: NetherlandsDeadline: 2025-09-14Fields: Mechanical Engineering, Materials Science, Computer Science, Applied Mathematics, MetallurgyAre you passionate about leveraging machine learning to revolutionize materials engineering and contribute to a more sustainable industrial future? If your academic ambitions involve developing advanced computational models for real-world applications in metal forming, the University of Groningen’s PhD position in ML-based implementation of constitutive behavior of stainless steel may be the ideal next step in your research career. The integration of machine learning with traditional materials modeling is rapidly transforming manufacturing and engineering.

By joining this project, you will be at the forefront of innovation, creating hybrid models that can predict the complex behavior of advanced stainless steels—crucial for both industrial efficiency and environmental sustainability. About The University Or Research InstituteThe University of Groningen, located in the vibrant city of Groningen, Netherlands, is one of Europe’s leading research universities with a rich tradition of academic excellence dating back to 1614. The university is renowned for its strong international orientation, interdisciplinary research environment, and commitment to addressing global challenges.

The Graduate School of Science and Engineering provides a dynamic and supportive setting for doctoral candidates, fostering innovation and collaboration across fields. Research Topic and SignificanceThe main focus of this PhD project is the development of hybrid, model- and data-driven machine learning (ML) models to capture the constitutive behavior of stainless steel. Metal forming is a core process in manufacturing, and as steel is highly recyclable, it plays a pivotal role in sustainable production. However, advanced stainless steels exhibit complex, nonlinear, and history-dependent behaviors due to their intricate microstructures and multiple phases.

Accurately modeling these behaviors is essential for predicting material performance, especially as the steel industry transitions toward greener production methods. Also SeePhD in Systems Engineering for Sustainable Energy in Manufacturing at TU DelftPhD Position on the Transition to a Green Steel IndustryPhD Positions in Advanced High-Temperature Composites for Aerospace at UNSW SydneyPhD Position in ML-Accelerated Simulations and Uncertainty Quantification of Sustainable…Postdoctoral Opportunities in Advanced Materials at KFUPMTraditional simulation methods, such as continuum models combined with the Finite Element Method (FEM), rely on constitutive relations to describe material behavior.

The emergence of new steel production processes, particularly those aimed at sustainability, demands precise predictive models to ensure industrial viability. By integrating advanced ML techniques with FEM, this project aims to replace labor-intensive experimental procedures, streamlining both time and cost in industrial pipelines. Project DetailsThis Full-time, Four-year PhD Position (1. 0 FTE) Will Focus On The ML-based Implementation Of Constitutive Models For Stainless Steel. The Project’s Objectives Include– Developing hybrid material models that combine traditional modeling with advanced machine learning algorithms. – Capturing complex, nonlinear, path- and history-dependent material behaviors.

– Seamlessly integrating these models into FEM simulations to enhance predictive accuracy and efficiency. – Supporting the transition to sustainable steel production by enabling high-precision property prediction for new materials. The successful candidate will join a multidisciplinary team and enroll in the Graduate School of Science and Engineering. The position offers a competitive salary, holiday and year-end bonuses, and a structured training program. The initial contract is for one year, with the possibility of renewal for an additional three years based on satisfactory progress.

Candidate ProfileThe Ideal Applicant Will Possess– A master’s degree in mechanical engineering, materials science, computer science, applied mathematics, or a closely related field. – A robust background in machine learning, material modeling, and metals processing, modeling, and simulation (highly advantageous). – Proficiency in programming languages such as Python or MATLAB, as well as experience with simulation software. – A dynamic, creative, and pioneering attitude, with a willingness to work in a strongly interdisciplinary team and learn cross-disciplinary foundations. – Excellent communication skills, with fluency in English (both oral and written). English proficiency must be demonstrated by one of the following:

– An overall IELTS (academic version) score of 6. 5 or higher. – A minimum score of 237 on the computer-based TOEFL. – A minimum score of 92 on the internet-based TOEFL. Application ProcessApplications Must Include– A letter of motivation. – A curriculum vitae with contact information for at least two academic references. – Transcripts from both Bachelor’s and Master’s degrees. – A list of publications (if available). Applications Should Be Submitted Via The Application Form Available On The University’s Website. To Apply, Click “Apply” On The Advertisement At The Following Linkhttps: //www. rug. nl/about-ug/work-with-us/job-opportunities/?details=00347-02S000BEXPThe application deadline is 14 September 2025, 11:

59pm (before 15 September 2025). The preferred starting date for the position is 1 January 2026. ConclusionThis PhD opportunity at the University of Groningen offers a unique platform to contribute to the advancement of sustainable manufacturing through cutting-edge research at the intersection of machine learning and materials science. If you are driven by innovation and ready to tackle complex challenges in industrial materials modeling, you are strongly encouraged to apply. For more such positions and academic career opportunities, explore the links below this post. Want to calculate your PhD admission chances? Try it here: https: //phdfinder.

com/phd_admission_chance_calculator/ Get the latest openings in your field and preferred country—straight to your email inbox. Sign up now for 14 days free: https: //phdfinder. com/position-alert-service/We’re an independent team helping students find opportunities. Found this opportunity helpful? Support us with a coffee!