Computational Materials Researcher

1. Join a scientific computing research team with diverse backgrounds to use density functional theory and molecular dynamics simulations to study battery material discovery and accelerate the development of machine learning force fields 2. Innovate and improve new methods, including: 1) Run MD simulations and optimize the prediction of battery material properties 2) Generate training data using DFT of machine learning force fields 3) Develop and improve machine learning force field models 3. Conceive, develop and evaluate comprehensive solutions to scientific challenges.

1. PhD degree in computational chemistry, materials science, physics and other related majors 2. Good communication and teamwork skills, active willingness to learn and serious and responsible work attitude 3. Proficient in at least one programming language (Python/C/C++), with a solid programming foundation, good programming style and work habits 4. Proficient in at least one mainstream MD software (LAMMPS or Gromacs), or at least one mainstream DFT software 5. Proficient in at least one mainstream deep learning framework (Pytorch/Tensorflow/JAX).