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Machine learning for catalyst material design
Job Number: P23INT-35
This project is about using machine learning to aid the catalyst material design. It involves data collection, database curation, material property simulation, prediction, validation, and optimization.
San Jose, CA
| Duration |
6 Months |
| Position Introduction |
This project is about using machine learning to aid the catalyst material design. It involves data collection, database curation, material property simulation, prediction, validation, and optimization.
|
| Key Responsibilities |
During the time of the internship, you are expected to
- Assist material data collection and database curation
- Assist machine learning algorithm development for material property prediction
- Assist machine learning algorithm development for material optimization
- Assist validation of material properties
|
| Minimum Qualifications |
- M.S. degree in computer science, electrical engineering, material science, chemistry, chemical engineering, physics, or related fields
- Development experience in data-driven optimization
- Knowledge of electrochemical material and experience in experiments
- Strong skills in Python and ML tools
|
| Bonus Qualifications |
- Ph.D. candidate in computer science, electrical engineering, material science, chemistry, chemical engineering, physics, or related fields
- Research experience in Bayesian optimization
- Research experience in graphical neural networks
- Research experience in machine learning for material design
- Experience in electrochemical material simulation
- Experience in electrochemical material experimental testing
|
| Years of Work Experience Required |
0 years |
| Position Keywords |
material science, catalyst, chemistry, data-driven optimization, Bayesian optimization, machine learning
|
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