AI Molecular Dynamics Algorithm Researcher Graduate (Seed AI for Science - Atomistic AI) - 2027 Start (PhD)

About the Team

The AI for Science team has been focusing on tackling challenges in natural sciences, including biology, physics, and materials, with computational tools such as Machine Learning, Computational Chemistry, High-throughput Computation. Our goal is to create breakthroughs in natural science with new methodology and help the world. We are looking for an AI Molecular Dynamics Algorithm Researcher to join a multidisciplinary scientific computing research team and tackle challenging problems in biology, materials science, and related domains.

We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume.

Responsibilities

• Join a research team with diverse backgrounds in scientific computing and help solve challenging problems in fields such as biology and materials science.
• Stay up to date with the latest developments in AI for Science, including progress in biology, materials science, quantum chemistry, and intelligent agents.

Qualifications

Minimum Qualifications

• Individuals who are completing or have recently completed a PhD degree in Computer Science, Mathematics, Physics, Chemistry, Materials Science, or related disciplines.
• Strong communication and teamwork skills, a proactive willingness to learn, and a conscientious, responsible work ethic.
• Proficiency in at least one programming language, such as Python, C, C++, CUDA, or Triton, with solid engineering capability and the ability to effectively orchestrate coding agents in development workflows.
• Meets at least one of the following criteria: Familiar with quantum chemistry algorithms, with experience in developing or using DFT/Post-HF computational tools or algorithms；Familiar with molecular dynamics engine development, with experience modifying the source code of LAMMPS or OpenMM；Familiar with machine learning algorithms, with hands-on experience in at least one of the following areas: LLMs, reinforcement learning, agents, or generative models；Familiar with solid-state physics, with hands-on experience in materials simulation and MLIP development；Familiar with statistical mechanics and enhanced sampling algorithms, with strong domain knowledge and development capability.
• Candidates with publications in top journals in related fields and/or strong industry experience will be preferred.

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