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Studying of Materials Synthesis with NLP and Machine Learning Techniques

 

The successful candidate will employ and extend Machine Learning and Natural Language Processing techniques to train computer models for understand materials synthesis papers, and apply these models to discover, deliver and verify new insightful knowledge on materials synthesis theory. The ultimate goal of this project is to construct a comprehensive theory that may predict and guide experimental designs in materials synthesis. The position requires:

 

• Basic level of understanding in machine learning.
• Basic understanding of thermodynamics, phase diagrams, common materials synthesis procedures, etc.
• Adequate experience in computer programming and software development, preferably in Python.
• Team working skills.

 

Experience in the following areas is a plus:

 

• Background in physics, chemistry related fields.
• Knowledge in Natural Language Processing.
• Database administration.
• Basic knowledge in website technologies (HTMLs etc.)
• Github.
• Familiarity with one deep learning software framework (tensorflow, keras, pytorch)

 

Please send the cover letter and resume to [email protected].

For more information about our research group, please check http://ceder.berkeley.edu/contact-us/openings/