SD- 26053 PHD IN ULTRA-FAST MACHINE-LEARNING INTERATOMIC POTENTIALS FOR NANOINDENTATION OF TIC MATERIALS

We seek a highly motivated and capable PhD candidate to develop and apply ultra-fast machine-learning interatomic potentials (UFPs, Xie et al., npj Comput. Mater., 2023, 10.1038/s41524-023-01092-7) for long, multi-million-atom molecular dynamics (MD) simulations of different materials families composed of Ti and C. Titanium carbides, for example, exhibit exceptional hardness, high melting point, wear and abrasion resistance, and many other beneficial properties; their industrial applications include hard alloys and ceramic-metal composites for cutting and wear-resistant tools, protective coatings, and furnace and aerospace turbines. Together, we will identify and push the boundaries of UFPs to simulate the mechanical properties of this family of materials, in particular, hardness via nanoindentation, in agreement with and beyond experimental results.

Do you want to know more about LIST? Check our website: https://www.list.lu/

Your LIST benefits

• An organization with a passion for impact and strong RDI partnerships in Luxembourg and Europe that works on responsible and independent research projects
• Sustainable by design, empowering our belief that we play an essential role in paving the way to a green society
• Innovative infrastructures and exceptional labs occupying more than 5,000 square metres, including innovations in all that we do
• An environment encouraging curiosity, innovation and entrepreneurship in all areas
• Personalized learning programme to foster our staff’s soft and technical skills
• Multicultural and international work environment with more than 50 nationalities represented in our workforce
• Diverse and inclusive work environment empowering our people to fulfil their personal and professional ambitions
• Gender-friendly environment with multiple actions to attract, develop and retain women in science
• 32 days’ paid annual leave, 11 public holidays, 13-month salary, statutory health insurance
• Flexible working hours, home working policy and access to lunch vouchers

Temporary contract | 14 + 22 + 14 months | Belvaux

Are you fascinated by data-driven atomistic simulations for materials science? So are we! Come and join us.

How will you contribute?

You will model the mechanical properties and plastic deformation of materials with diverse compositions of Ti and C and different structures, including MD-based nanoindentation simulations and defect analyses. To facilitate this, you will further develop the ultra-fast machine-learning potentials used, both methodologically and in implementation.

Is Your profile described below? Are you our future colleague? Apply now!

We are looking for candidates with:

• A pertinent master’s degree in computational materials science or a related discipline
• Some knowledge of the theory of materials and experience with computational methods in materials science
• Some experience with machine-learning interatomic potentials
• Good programming skills in Python
• A friendly, motivated, positive, hands-on, initiative-taking, collaborative attitude and demeanour
• Fluency in English

Any of the following will be a plus:

• Experience developing machine-learning interatomic potentials
• Experience with UFPs
• Experience with molecular dynamics, ideally with LAMMPS
• Contributions to a public code repository