Computational Scientist

Computational Materials Scientist (Atomistic & Quantum Modelling)

A fast growing R&D group working at the intersection of advanced batteries, physics based simulation, and scientific machine learning is looking for a Computational Materials Scientist. The team builds next generation lithium metal and lithium ion technologies by combining atomistic modeling, quantum methods, and AI driven prediction tools.

You will work with researchers and engineers who are pushing the limits of materials discovery. Your work will directly influence new electrolytes, coatings, and electrode designs for high energy battery systems across mobility, aviation, robotics, and grid storage.

• Run and automate DFT, MD, and quantum simulations for battery materials.
• Build ML enhanced force fields and surrogate models to speed up multi scale simulation work.
• Generate high quality simulation datasets for training scientific ML models.
• Support the creation of domain aware LLM features for materials and battery property prediction.
• Use tools such as VASP, Quantum Espresso, and Python to manage large scale simulation workflows.
• Collaborate with experimental teams to validate models and guide design decisions.

• PhD in Materials Science, Mechanical Engineering, Chemical Engineering, Physics, or a similar field.
• Strong experience in atomistic and quantum modeling.
• Hands on use of DFT and MD tools such as VASP or Quantum Espresso.
• Experience modeling electrochemical energy materials.
• Strong Python skills for workflow automation and data analysis.
• Experience with ML assisted force fields or surrogate models.

• Exposure to LLM based property prediction or scientific agents.
• Experience in hybrid computational plus experimental settings.
• Skilled with additional data analysis tools such as MATLAB, SQL, or R.
• Background from national labs or advanced battery modeling groups.

Mid‑Senior level

Full‑time

Engineering and Information Technology