Machine Learning Research Scientist Intern (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 talented individuals to join us for an internship.

PhD internships at Our Company provide students with the opportunity to actively contribute to our products and research, as well as to the organization's future plans and emerging technologies.

Our dynamic internship experience blends hands-on learning, enriching community-building and professional development events, and collaboration with industry experts.

Applications will be reviewed on a rolling basis, so we encourage you to apply early.

Please clearly state your availability in your resume (Start date, End date).

Track cutting-edge research developments across the industry and work closely with the team to build deep and broad technical understanding of the field. 2.

Integrate methods from multiple disciplines, including machine learning, quantum chemistry, and molecular dynamics, to explore frontier applications in biology and materials science. 3.

Consolidate research outcomes from both the industry and internal teams, and help translate research into practical applications with broad impact.

Minimum Qualifications: - Currently pursuing a Ph.D.; preferred majors include Computer Science, Mathematics, Physics, Computational Materials Science, Quantum Chemistry, or related fields. - 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.