About the position
Postdoc Predicting & Mitigating Liquid Copper Infiltration in Steels via Atomistic Simulations
Is the job funded through the EU Research Framework Programme?: Not funded by a EU programme
Is the Job related to staff position within a Research Infrastructure?: No
As a postdoctoral researcher, you will unravel how silicon suppresses liquid copper infiltration at the atomic scale, using density functional theory-accurate machine-learned potentials and molecular dynamics simulations, in close collaboration with leading European research institutes and steel industry partners.
This project addresses a critical and growing challenge in metallurgy which is the liquid copper infiltration (LCI) in copper-contaminated steels leading to cracking during steel processing.
Team Dey within the Computational Materials Science section at TU Delft is actively engaged in developing fundamental understanding for next-generation circular steelmaking.
Where experimental work within the project focuses on process development and validation, this position addresses the underlying governing atomistic mechanisms.
Within this position, you will employ molecular dynamics (MD) simulations to investigate the underlying atomistic mechanisms of LCI in steel grain boundaries and the inhibitory role of silicon.
In this role, you will develop fundamental insights into the atomistic mechanisms governing LCI at steel grain boundaries and the inhibitory role of silicon in copper-contaminated steels.
Your project on the atomistic mechanisms of liquid copper infiltration (LCI) in steels and the inhibitory role of silicon aligns with the team's broader interest in metal–impurity interactions, interfacial phenomena and its commitment to computation-guided design for green and circular steel production.
You will collaborate closely with researchers from the broader research programme, including experimental teams and key partners such as Leibniz-Institut Für Werkstofforientierte Technologien (IWT), Oulun Yliopisto, Thyssenkrupp Steel Europe AG and Ovako Sweden AB.The Computational Materials Science section offers a collaborative and intellectually stimulating environment, where researchers work across disciplines and scales, with ample opportunities for scientific development and impact.
We are looking for a self-motivated researcher to help develop atomistic insights and simulation tools for enabling LCI-resistant steels.
You have a strong expertise in atomistic and molecular simulation techniques (density functional theory, molecular dynamics, ab initio molecular dynamics) and in developing machine learning interatomic potentials and can apply these to uncover atomistic mechanisms relevant to LCI mitigation, such as copper–silicon competition at grain boundaries, copper trapping by the formation of intermetallic phases, etc.
Join this unique programme, where you can apply your technical knowledge to collaborate with leading European research institutes and steel industries.
https://www.academictransfer.com/en/jobs/363299/postdoc-predicting-mitigating-l…
What you’ll bring
- You hold a PhD degree in Materials Science and Engineering, Physics, Chemistry, or a closely related discipline.
- You have a strong expertise in atomistic and molecular simulation techniques (density functional theory, molecular dynamics, ab initio molecular dynamics) and in developing machine learning interatomic potentials and can apply these to uncover atomistic mechanisms relevant to LCI mitigation, such as copper–silicon competition at grain boundaries, copper trapping by the formation of intermetallic phases, etc.
- You have a strong track record in scientific research, as evident from publications in peer-reviewed international journals and conference participation.
- You have excellent written and verbal communication skills in English.
At a glance
- Position type
- Postdoc
- Institution
- Delft University of Technology (TU Delft) / TU Delft
- Department
- Not stated
- Research group
- Not stated
- Location
- Delft, Netherlands, NL
- Supervisor / contact person
- Not stated
- Funding
- Funding not stated
Is the job funded through the EU Research Framework Programme?: Not funded by a EU programme - Duration
- Not stated
- Expected start
- Not stated
- Vacancy reference
- EURAXESS-460402
Research focus
- Discipline
- Materials Science, Chemistry, Physics, Mechanical Engineering
- Research area
- Metallurgy, Surfaces & interfaces
- Methods
- DFT, Molecular dynamics (MD)
- Software
- Not stated
Dates to know
- Listed on FOSS Positions
- 17 Sep 2026
- Original advertisement date
- Not stated in the source
- Application deadline
- 21 Sep 2026 · 21:59 UTC
- Priority review date
- Not stated
- Last checked
- 17 Sep 2026
- Last updated
- 17 Sep 2026
2026-09-21T21:59:59+00:00
How to apply
Application Deadline: 21 Sep 2026 - 21:59 (UTC)
You have a strong expertise in atomistic and molecular simulation techniques (density functional theory, molecular dynamics, ab initio molecular dynamics) and in developing machine learning interatomic potentials and can apply these to uncover atomistic mechanisms relevant to LCI mitigation, such as copper–silicon competition at grain boundaries, copper trapping by the formation of intermetallic phases, etc.
Join this unique programme, where you can apply your technical knowledge to collaborate with leading European research institutes and steel industries. Imagine enabling the safe use of recycled scrap in green steel production and improving the circularity of steel. You can help make an impact on a more sustainable future.
From chip to ship. From machine to human being. From idea to solution. Driven by a deep-rooted desire to understand our environment and discover its underlying mechanisms, research and education at the ME faculty focusses on fundamental understanding, design, production including application and product improvement, materials, processes and (mechanical) systems.
Where to apply
Apply now
https://www.academictransfer.com/en/jobs/363299/postdoc-predicting-mitigating-liquid-copper-infiltration-in-steels-via-atomistic-simulations/apply/
Original sources
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