Artificial Intelligence

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 is expected, and in particular with deep learning algorithms for image processing and object detection. 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. 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. 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, with a flexible starting date.

Required Specific Experience

• Python, PyTorch, OpenCV

MERL is seeking a motivated and qualified individual to work on fast surrogate models for sound emission and propagation from complex vibrating structures, with applications in HVAC noise reduction. The ideal candidate will be a PhD student in engineering or related fields with a solid background in frequency-domain acoustic modeling and numerical techniques for partial differential equations (PDEs). Preferred skills include knowledge of the boundary element method (BEM), data-driven modeling, and physics-informed machine learning. Publication of the results obtained during the internship is expected. The duration is expected to be at least 3 months with a flexible start date.

MERL is looking for a highly motivated individual to work on designing robot motor skills for contact-rich dexterous manipulation such as tool manipulation using visuotactile sensing. Through the internship, we develop closed-loop controller for dexterous manipulation that can stabilize manipulation even under unexpected contact events. A successful internship will result in the submission of results to a peer-reviewed robotics conference papers (e.g., RSS, ICRA, CoRL) or journal in collaboration with MERL researchers.

The expected duration of this internship is around 3 months with a start date in the Summer/Fall of 2025. This internship will be onsite at MERL.

Required Specific Experience

• Senior PhD students in robotics and engineering related field.

• Either (1) experience in machine learning for robotic manipulation such as reinforcement, representation, and imitation learning, with an emphasis on real-world deployments, or (2) experience in model-based optimization for contact-rich robotic manipulation such as trajectory optimization, MPC, and estimators.

• Experience in visuotactile sensors (e.g., GelSight, GelSlim, DIGIT)

• Familiarity with related frameworks in either (1) Machine Learning (e.g., PyTorch), or (2) model-based optimization (e.g., IPOPT, Gurobi).

• Experience working with physical hardware systems is required.

• Prior publication experience in robotics venues such as ICRA, RSS, IROS, and CoRL.

Additional Desired Experience

• Experience in computer vision.

• Experience in dexterous manipulation such as bimanual manipulation, tool manipulation, and whole-body manipulation.

• Familiarity with robotic simulators (e.g., MuJoCo).

• Experience in scalable software development (e.g., CI, unittests, OOP) in Python or C++.