Researcher in Modelling for Materials Design Full-time NEW

Researcher in Modelling for Materials Design

At the Hultgren Laboratory at the Department of Materials Science and Engineering, we are seeking a researcher with a strong interest in developing and applying machine‑learning methods for materials design, in particular steel design. The position is part of our growing research environment focused on physics‑informed AI tools for materials design and characterization.

In a collaborative project with industrial partners, you will work on developing physics‑informed models for heat treatment of steels. A key part of the physics‑informed approach will be to make use of thermodynamics‑based understanding, a long‑standing core expertise at KTH, to guide model development and ensure physically consistent predictions. You will contribute to models that link composition, processing, microstructure and properties, and support novel alloy and process design targeting key industrial challenges such as electrification and circular production routes using recycled feedstock.

Experimental input data and validation will also be an important part of the work. You will have the opportunity to develop understanding of experimental methodologies and their limitations, in close collaboration with experimental experts at the Hultgren Laboratory.

The position is well suited for candidates with a background in computational materials science and engineering or related fields, who wish to deepen their expertise in physics‑informed machine learning for materials design and work closely with industry and leading academics in steel development. You will have excellent opportunities to contribute to shaping next‑generation digital tools for sustainable steel design.

Qualifications

Requirements

• A doctoral degree or equivalent foreign degree in computational materials science and engineering or a closely related area.
• Research experience in machine learning within materials science, demonstrated through journal publications and international conference presentations.
• Experience from programming with e.g. Python.
• Ability to express yourself very well in English, in speech and writing, as this will be required in day-to-day work.
• You are independent, self‑motivated and able to take initiative and drive projects.
• Collaborative abilities and experience working in interdisciplinary environments.
• Awareness of diversity and equal opportunity issues, with specific focus on gender equality.
• Ability to communicate research and development work clearly.

Preferred qualifications

• Doctoral degree or equivalent foreign degree obtained within the last three years prior to the application deadline.
• Experience in method development involving integration of physics approaches and data‑driven approaches as well as experiments.
• Potential and interest to become a leader in machine learning for materials design and characterization in a mainly experimental research environment.

Great emphasis will be placed on personal skills.