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OR0180: Internship - System Identification
MERL is looking for a highly motivated and qualified PhD student in the areas of system identification, to participate in research on advanced algorithms for system identification of mechanical systems and processes. Solid background and hands-on experience with various system identification algorithms is required, including black-box and grey-box methods. Good understanding of mechanics is expected, as well as familiarity with algorithms for computing forward and inverse dynamics of mechanical systems. Of particular help would be expertise in tribology and metal-cutting technology. Hands-on experience with physics engines and other simulators would be a plus. Solid experimental skills and hands-on experience in coding in Python is required for the position. A more general understanding of machine learning algorithms, including deep learning, and experience with relevant libraries, such as scikit-learn and PyTorch would be considered a plus. Knowledge of time series analysis methods, in particular anomaly detection in time series, would also be beneficial. Hands-on skills in data acquisition from physical systems is desirable, but not strictly required.
The position will provide opportunities for exploring fundamental problems in system identification leading to publishable results. The duration of the internship is 3 to 5 months. Preference will be given to candidates who can start in the Fall of 2025 and no later than the beginning of January 2026.
Required Specific Experience
- System identification algorithms
- Classical mechanics
- Python
Desired Specific Experience
- Modelling of metal cutting
- Tribology
- Time series analysis
- Machine learning
- Physics engines
- Data acquisition
The pay range for this internship position will 6-8K per month.
- Research Areas: Data Analytics, Machine Learning
- Host: Daniel Nikovski
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