Classification Title: Post-Doctoral Associate in unsupervised and generative ML for chemistry

Classification Minimum Requirements: 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.

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.

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.

Classification Title: Post-Doctoral Associate in unsupervised and generative ML for chemistry

Classification Minimum Requirements: 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.

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.

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.