Chemistry Post-Doctoral Associate - Unsupervised and Generative Machine Learning
This position is initially awarded for one year, and may be renewed for up to two additional years contingent upon strong performance, conduct, and availability of funds.
About the role
A Post-doctoral Associate in Theoretical Chemistry is available for work on a project in unsupervised and generative machine learning for chemical applications led by Dr. Ramon Miranda Quintana in the Department of Chemistry.
The role focuses on the development, implementation, and application of hyper-efficient unsupervised learning techniques to chemical problems, with emphasis on improvements to representation learning and generative methods.
Responsibilities
- Develop and implement unsupervised learning techniques for chemical applications.
- Advance representation learning and generative methods in chemistry.
Requirements
- PhD in Chemistry or a related area.
- Strong coding skills in Python and algorithm design.
- Expertise in machine learning tools for chemistry, particularly generative AI.
- Experience with Python, machine learning, and AI for chemical applications.
Qualifications
- Familiarity with high-performance computing (HPC) systems.
- Proven track record of research in machine learning/AI for chemistry.
- Strong foundation in Python programming.
- Knowledge of C++ and CUDA.
Pay
The salary is competitive and commensurate with qualifications and experience, including a full benefits package. Learn more about benefits.