General Summary of Position:
The undergraduate summer intern will work on a collaborative project that seeks to develop materials for next generation molten salt nuclear reactors. This work will involve the use physics-based simulations and the development of machine learning models for improving prediction of material and fluid properties.
The student will work with Professor Stephen Lam and a PhD student to train and test state-of-the-art machine learning models that are capable of rapidly predicting material properties. Student will be expected to
- Write python code to automate workflows, perform data mining, data analysis, test models
- Attend weekly group meetings to discuss progress and results
- Review literature and manuals to learn python programming, accessing materials databases, machine learning packages (e.g., sci-kit learn, pytorch)
Learning objectives include becoming proficient object-oriented programming (python), and developing understanding of supervised machine learning (model inputs, outputs, hyperparamters, how to train and optimize models). This work can support honors project, undergraduate thesis, independent studies course, etc.
Minimum Qualifications:
- Programming experience in python
- HS Diploma or GED/equivalent and pursuing an undergraduate degree in engineering, materials science, physics, chemistry or other related field.
- Basic data analysis, visualization, and processing
Preferred Qualifications:
- Interest in materials science, machine learning, artificial intelligence
- Experience with atomistic simulation, density functional theory, molecular dynamics
Special Instructions to Applicants:
Initial review of applications will begin immediately and continue until the position is filled. However, the position may close when an adequate number of qualified applicants is received.
This is a part-time, temporary, non-unit, non-benefited position.
Please include a resume with your application. Names and contact information of three references will be required during the application process. Submission of a cover letter is optional.
