Job Summary

This is a part-time, temporary position in Dr. Eiler’s laboratory. The position is tasked with creating and testing computational algorithms that apply machine learning, as well as simpler concepts of statistical fitting, to models of the effects of isotopic substitution on molecular chemical properties. The successful candidate will work directly with graduate students, postdoctoral scholars, and staff scientists in the laboratories for stable isotope geochemistry, so that their model algorithms can directly address needs of the research group.

Job Duties

• Creation of Python-based computer algorithms for statistical fitting and machine learning.
• Use and modification of existing Python-based computer algorithms designed for modeling chemical structures and processes.
• Collaboration with students, postdoctoral scientists, and scientific staff in the laboratories for stable isotope geochemistry.
• Weekly verbal or written reporting of progress.
• Other duties as assigned.

Basic Qualifications

• Formal training in computer programming and machine learning.
• Prior experience in computer programing for scientific applications.
• Knowledge of machine learning algorithms; statistics.
• Skills: Code writing in Python; construction of machine learning algorithms; Monte Carlo-based model fitting.

Required Documents

• Resume