Daniel N. Nikovski

MERL is looking for a highly motivated and qualified PhD student in the areas of machine learning and robotics, to participate in research on advanced algorithms for learning control of robots and other mechanisms. Solid background and hands-on experience with various machine learning algorithms, including deep learning, is expected, as well as good understanding of computer vision methods, in particular algorithms for keypoint detection and tracking. Exposure to deep reinforcement learning and/or learning from demonstration is highly desirable. Familiarity with the use of machine learning algorithms for system identification of mechanical systems would be a plus, along with background in other areas of automatic control. Familiarity with visual servocontrol is highly desirable. Solid experimental skills and hands-on experience in coding in Python, PyTorch, and OpenCV are required for the position. Some experience with ROS2 and familiarity with classical mechanics and computational physics engines would be helpful, but is not required. Hands-on familiarity with industrial robots will be a definite plus. The position will provide opportunities for exploring fundamental problems in incremental learning in humans and machines, leading to publishable results. The duration of the internship is 3 to 5 months. Preference will be given to candidates who can start no later than the beginning of January 2025.

Required Specific Experience

• Python, PyTorch, OpenCV
• Keypoint tracking in images

Desired Specific Experience

• Visual servocontrol of robots
• Learning diffusion policies
• MuJoCo or other physics engines
• System identification
• Clustering algorithms
• ROS2

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

Mitsubishi Electric Research Labs, Inc. "MERL" provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability or genetics. In addition to federal law requirements, MERL complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.

MERL expressly prohibits any form of workplace harassment based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. Improper interference with the ability of MERL’s employees to perform their job duties may result in discipline up to and including discharge.

Working at MERL requires full authorization to work in the U.S and access to technology, software and other information that is subject to governmental access control restrictions, due to export controls. Employment is conditioned on continued full authorization to work in the U.S and the availability of government authorization for the release of these items, which might include without limitation, obtaining an export license or other documentation. MERL may delay commencement of employment, rescind an offer of employment, terminate employment, and/or modify job responsibilities, compensation, benefits, and/or access to MERL facilities and information systems, as MERL deems appropriate, to ensure practical compliance with applicable employment law and government access control restrictions.