MERL is looking for a self-motivated and qualified candidate to work on modeling for contact phenomenon. Robotic manipulation is heavily affected by external contacts that can be modeled with physical and data driven models. We are interested in researching those models for analysis and control purposes. The ideal candidate is a PhD student and should have experience and records in multiple of the following areas. Contact modeling and robotic manipulation. Physic Engines like Mujoco, Bullet, Drake and sim2real gap problems. Machine learning techniques for modeling and control such as Gaussian Processes and Neural Networks. Experience in working with robotic systems. Knowledge in learning from demonstration algorithms and standard Reinforcement Learning algorithms is a plus. Proficiency in Python is required. The successful candidate will be expected to develop, in collaboration with MERL employees, state of the art algorithms that will lead to a scientific publication. Typical internship length is 3-4 months.

MERL is seeking an intern to work on research for human-machine interface with multi-modal bio-sensors. The ideal candidate is an experienced PhD student or post-graduate researcher having an excellent background in brain-machine interface, deep learning, mixed reality, bio sensors and sensing as well as signal processing.

MERL is offering an internship position for non-research software development for robotic manipulation. The scope of the internship is to develop robust ROS packages by refactoring existing experimental code. The position is open to prospective candidates with very strong programming skills in ROS (Robot Operating System) using C++ primarily and Python respectively. The selected intern will have a software engineering role rather than research oriented. The position is open to both senior undergraduate students and master students. Flexible start and end dates.