The Norwegian University of Science and Technology (NTNU) invites applications for a fully funded PhD position in the field of machine learning for crystal and continuum plasticity. This exciting opportunity combines materials science, computational modeling, and machine learning to tackle pressing challenges in understanding and predicting material behavior.

The PhD position is based in the Physical Metallurgy Group at the Department of Materials Science and Engineering, offering an opportunity to advance your academic career in a world-class research environment.

About the Research

Crystal plasticity is central to understanding the local and global behavior of materials, especially in metals and alloys, under various loading conditions. This PhD project focuses on advancing crystal and continuum plasticity modeling by integrating machine learning techniques, such as recurrent neural networks, into constitutive equations. This work aims to improve simulations of forming and forging processes, contributing to material optimization and industrial applications.

Key areas of investigation include:

• Application of machine learning methods in crystal and continuum plasticity.
• Development of computational models for material behavior during forming and forging.
• Collaboration with industry partners to validate findings and apply them to real-world problems.

Your Responsibilities

As a PhD candidate, your main tasks will include:

• Conducting original research on applying machine learning in crystal and continuum plasticity.
• Developing computational models to predict material behavior.
• Collaborating with researchers and industry partners to validate and apply findings.
• Publishing research findings in high-quality journals and presenting at international conferences.
• Contributing to academic activities, including supervising undergraduate and master’s students.

Eligibility Requirements

Applicants must meet the following criteria:

1. Academic Background
   • Master’s degree in materials science, mechanical engineering, applied physics, or mathematics.
   • A strong academic record equivalent to a grade of B or better on the NTNU grading scale.
2. Technical Skills
   • Proficiency in machine learning techniques and computational modeling.
   • Experience with programming languages such as Python, FORTRAN, or similar tools.
3. Additional Requirements
   • Good written and oral English communication skills.
   • Eligibility for admission to NTNU’s doctoral program in materials science and engineering.

Preferred Qualifications

• Strong background in materials science, mechanical engineering, or applied physics.
• Experience with FE software and user-defined material subroutines.
• Knowledge of Scandinavian languages is an advantage.

What We Offer

NTNU provides:

• Exciting research opportunities in a leading international academic environment.
• A supportive, open, and inclusive workplace with dedicated colleagues.
• A gross annual salary starting at NOK 532,200 (approximately €45,000), with 2% deducted for the Norwegian Public Service Pension Fund.
• Access to employee benefits, including subsidized day care and wellness programs.
• Opportunities for professional development and international networking.

The position is fully funded for 3 years, with a preferred start date in August 2025.

Application Process

To apply, submit the following documents via jobbnorge.no:

1. Cover Letter outlining your motivation, scientific experience, and suitability for the position.
2. A draft research proposal (1-2 pages) related to the project description.
3. CV including relevant work and academic experience.
4. Copies of transcripts and diplomas for bachelor’s and master’s degrees.
5. A copy of your master’s thesis or a draft if not yet completed.
6. Documentation of English proficiency (if applicable).
7. Contact information for three referees.
8. Any relevant publications or scientific work.

Applications must be submitted in English. Shortlisted candidates may be invited for interviews and asked to provide additional documentation.