MERL is seeking an intern to work on machine learning for wireless communication systems. The ideal candidate would be an experienced PhD student or post-graduate researcher working in wireless communications with a focus on machine learning methods. The candidate should have a detailed knowledge of wireless communications, with some experience in machine learning, MIMO, and/or channel equalization preferred. Strong programming skills in Python and machine learning frameworks are essential. The expected duration of the internship is 3-6 months with flexible start date and length. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.

MERL is looking for a self-motivated and qualified candidate to work on robotic manipulation projects. The ideal candidate is a PhD student and should have experience and records in multiple of the following areas. Machine learning techniques for modeling and control such as Gaussian Processes and Neural Networks. Knowledge of standard Reinforcement Learning algorithms. Experience in working with robotic systems and familiarity with one physics engine simulator like Mujoco, pyBullet, pyDrake. Proficiency in Python is required. The successful candidate will be expected to develop, in collaboration with MERL employees, state of the art algorithms to solve complex robotic manipulation tasks that will lead to a scientific publication. Typical internship length is 3-4 months. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.

The Multiphysical Systems (MS) team at MERL is seeking a highly motivated intern to conduct research on data-driven modeling and control of HVAC systems, with special emphasis on transfer learning. The ideal candidate is enrolled in a PhD program and is pursuing research in learning and control. The ideal candidate will have experience in (one or more of) parameter estimation of dynamical systems, transfer learning or meta-learning, Bayesian optimization, and must be fluent in Python. The expected duration of the (virtual) internship is 3 months in Summer 2021; start-date is flexible (after April 2021). This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.