Develop and apply machine learning models for surrogate modeling of physical and engineering systems
Support optimization algorithms for recipe and hardware parameter tuning
Analyze simulation and experimental data to improve model accuracy and performance
Build Python-based workflows for model training, inference, and evaluation
Collaborate with engineers and scientists to translate engineering problems into data-driven models
Document methods and results and present findings to technical stakeholders

Currently pursuing a Bachelor’s degree in: Computer Science, Data Science, Electrical, Mechanical, or Chemical Engineering, Applied Mathematics or a related technical field
Strong programming skills in Python
Understanding of machine learning fundamentals
Coursework or hands-on experience in optimization, numerical methods, or scientific computing
Ability to work with data, debug models, and learn quickly

Data Science – Optimization Intern

Key skills