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Computational Chemistry PhD Internship
The key objectives of this project are to 1) develop advanced analysis techniques from explicit chemistry simulations using data mining, and pattern recognition techniques 2) develop predictive machine learning models to guide formulation development. The successful candidate will have skills in physical chemistry, simulations/computations, and/or machine learning and pattern recognition algorithms and will be pursuing a doctoral degree in Chemistry, Physics, Chemical Engineering, Material Science, or Computer Science. The specific project deliverables include:
• Development of automated analysis scripts and pattern recognition algorithms to identify non-intuitive collective variables in complex simulations.
• Application of developed algorithms to identify critical physicochemical properties of formulation components and/or individual raw materials that influence viscosity, membrane/micellar stability and partitioning.
Key Priorities, Deliverables, or Outcomes of This Position:
1) Development of data mining and automated scripts for the analysis of simulation data.
2) Development of pattern recognition and machine learning algorithms to identify non-intuitive collective variables in complex systems.
3) Identification of formulation collective variables, and/or individual raw material collective variables that drive changes in viscosity, micellar/bilayer stability and the partitioning characteristics.
1) Development of fundamental and applied skills in physical chemistry, colloid, and interface science
2) Consumer product formulation design
3) Application of technical and scientific skills in an industrial setting
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