Chemistry Post-Doctoral Associate - Unsupervised and Generative Machine Learning

Job Description:

A Post-doctoral Associate in Theoretical Chemistry is available for work on a project in unsupervised and generative ML for chemical applications led by Dr. Ramon Miranda Quintana in the Department of Chemistry. We have a position available in our group to work 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.

Must have PhD in Chemistry or related area. This position will be initially awarded for one year, and, contingent upon strong performance and conduct and availability of funds, may be renewed for up to two years.

Expected Salary:

The salary is competitive and commensurate with qualifications and experience, and the compensation includes a full benefits package. To see more, visit .

Required Qualifications:

PhD in Chemistry or related area

Strong coding (Python) and algorithm design skills

Expertise on ML tools for chemistry, in particular, generative AI

Experience with Python, ML, and AI for chemical applications

Preferred:

Familiarity with HPC systems

Proven track record of research in ML/AI for chemistry

Strong coding foundation (Python)

Knowledge of C++ and CUDA