Engineering Intern 1
Core
Design controlled synthetic test functions and conduct experiments to study and benchmark Bayesian surrogate modeling and optimization behavior.
Role type
Research intern (Machine Learning / Bayesian Optimization)
Builds
Synthetic benchmark functions, evaluation pipelines, and benchmark studies for ML models.
Domain
Semiconductor manufacturing / Machine Learning
Deliverable
research
Required skills
Statistical machine learning, probabilistic modeling, experimental design, Bayesian Optimization, Gaussian Processes, kernel methods, uncertainty quantification
Preferred skills
Python programming, PyTorch, GPyTorch, BoTorch, NumPy, Pandas, data representation, dimensionality reduction, visualization
Technologies
Python, PyTorch, GPyTorch, BoTorch, NumPy, Pandas
Responsibilities
Design configurable synthetic test functions with controlled properties; Evaluate Bayesian surrogate modeling enhancements; Explore data representation and dimensionality reduction; Characterize BO-loop behavior; Implement reusable components and evaluation pipelines; Develop and benchmark Gaussian Process models; Run controlled BO studies and analyze convergence; Document benchmark results and recommendations; Collaborate to integrate validated components into workflows.