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.
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
Salary and Benefits
The salary is competitive and commensurate with qualifications and experience, and the compensation includes a full benefits package.
Application Instructions
To apply, click on Apply Now at the top of this posting. A complete application includes (1) a letter of application summarizing the applicant's qualifications, interests, and suitability for the position, (2) a complete curriculum vitae, (3) a list of at least two references.