Tackling challenges in natural sciences, including biology, physics, and materials, with computational tools such as Machine Learning, Computational Chemistry, High-throughput Computation to create breakthroughs in natural science with new methodology and help the world.
Requirements
- Currently pursuing a PhD in Machine Learning, Computational Materials Science, Computational Biology, or a related field.
- Strong understanding of machine learning algorithms, with extensive hands-on experience and software development knowledge.
- Proven track record of publications in high-impact, peer-reviewed scientific journals.
- Demonstrated ability to conduct independent and innovative research; capable of thriving in a fast-paced, interdisciplinary environment.
- AI force field models and atomic/molecular foundational models
- Molecular dynamics enhanced sampling algorithms integrated with generative models
- Design of biomolecules or material molecules
Responsibilities
- Stay up to date with cutting-edge research and collaborate with the team to develop a broad and in-depth understanding of key technical domains.
- Apply interdisciplinary approaches—combining machine learning, quantum chemistry, molecular dynamics, and other methods—to explore novel applications in biology and materials science.
- Integrate internal and external research outcomes to drive real-world implementation of research achievements and create widespread impact.
Other
- Excellent teamwork and interpersonal communication skills, with the ability to convey complex ideas effectively.
- Please state your availability clearly in your resume (Start date, End date).
