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. This position will focus on the development, implementation, and application of hyper-efficient unsupervised learning techniques to chemical problems, with an emphasis on improvements to representation learning and generative methods. The position is initially for one year, with the possibility of renewal for up to two years based on performance, conduct, and funding availability.
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 in machine learning for chemistry.
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
