Postdoctoral Position in Molecular Stochastic Modeling, Dallas, TX

Postdoctoral Position in Molecular Stochastic Modeling, Dallas, TX, USA

The Tao Research Group in the Theoretical and Computational Chemistry (TCC) program at Southern Methodist University (SMU) invites applications for a Postdoctoral Research Fellow in molecular stochastic modeling. This is an exciting opportunity to contribute to cutting‑edge research focused on the development and application of stochastic differential equation (SDE) methods for modeling mechanically activated photochemistry (MAPC) and biomolecular processes. The successful candidate will play a key role in methodological development, with a particular focus on integrating machine learning techniques to manage high‑dimensional data and improve modeling accuracy.

Collaboration is a cornerstone of this position; the candidate will work closely with experimentalists in chemiluminescence and mechanochemistry, as well as with mathematicians specializing in stochastic differential equations and artificial intelligence.

• Develop and implement advanced SDE methods for modeling molecular‑level phenomena.
• Design and apply machine learning frameworks such as diffusion models and flow matching to enhance simulation accuracy and computational scalability.
• Leverage large‑scale simulation and experimental datasets to construct predictive models for MAPC and biomolecular mechanisms.
• Collaborate within a multidisciplinary team to integrate experimental insights into robust theoretical and computational models.
• Prepare high‑impact scientific manuscripts and present research outcomes at leading international conferences.

• Ph.D. in mathematics, chemistry, biophysics, physics, or a related discipline, with a strong emphasis on stochastic modeling and machine learning.
• Demonstrated proficiency in the development and application of stochastic differential equation frameworks for modeling complex stochastic processes.
• Strong programming skills in Python, R, or similar languages, and experience with high‑performance computing environments.
• Proficiency in machine learning, with an emphasis on generative AI techniques such as diffusion models and flow matching highly desired.
• Excellent analytical, organizational, and communication skills, with a proven track record of publishing scientific research.

The Tao Research Group is committed to excellence in research and education, offering a dynamic and inclusive environment for scientific discovery. Our research interests cover theoretical and computational methods development and their applications to biomolecular simulations, protein allostery, and enzyme catalysis. Located in Dallas, TX, the group operates at the heart of a thriving scientific community, providing ample opportunities for collaboration and innovation.

For more information, visit (Use the "Apply for this Job" box below)..

Interested candidates should submit a cover letter highlighting their research interests and experience, a detailed CV, and contact information for three references to Dr. Peng Tao via email at ptao+smu.edu. (Replace the '+' with '@' before sending.)