Chemistry Post-Doctoral Associate - Unsupervised and Generative Machine Learning
About The Position
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. 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.
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
Nice To Haves
- Familiarity with HPC systems
- Proven track record of research in ML/AI for chemistry
- Strong coding foundation (Python)
- Knowledge of C++ and CUDA
Responsibilities
- Development, implementation, and application of hyper-efficient unsupervised learning techniques to chemical problems.
- Improvements to representation learning and generative methods.
Benefits
- Full benefits package
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
Education Level
Ph.D. or professional degree
