The Control and Dynamical Systems (CD) group at MERL is seeking highly motivated interns at different levels of expertise to conduct research on planning and control for autonomous vehicles. The research domain includes algorithms for path planning, vehicle control, high level decision making, sensor-based navigation, driver-vehicle interaction. PhD students will be considered for algorithm development and analysis, and property proving. Master students will be considered for development and implementation in a scaled robotic test bench for autonomous vehicles. For algorithm development and analysis it is highly desirable to have deep background in one or more among: sampling-based planning methods, particle filtering, model predictive control, reachability methods, formal methods and abstractions of dynamical systems, and experience with their implementation in Matlab/Python/C++. For algorithm implementation, it is required to have working knowledge of Matlab, C++, and ROS, and it is a plus to have background in some of the above mentioned methods. The expected duration of the internship is 3-6 months with flexible start date after April 1st, 2020.

While deep networks have been highly successful at visual regression problems such as face landmark estimation and human body tracking, relatively little work has been done on estimating the uncertainty of their predictions. We are seeking a highly motivated intern to conduct original research in the area of uncertainty estimation for deep network predictions. The successful candidate will collaborate with MERL researchers to design and implement new models, conduct experiments, and prepare results for publication. The candidate should be a PhD student in computer vision and machine learning with a strong publication record and experience in deep learning-based face or body landmark estimation and tracking. Strong programming skills, experience developing and implementing new models in deep learning platforms such as PyTorch and TensorFlow, and broad knowledge of machine learning and deep learning methods are expected.

MERL is looking for a highly motivated intern to work on machine learning for computer vision. The ideal candidate would be a MS / Ph.D. student with a strong background in computer vision and machine learning. There are two projects to choose from: one in 3D reconstruction, and the other in sim2real/robotics. You are expected to collaborate with MERL researchers to develop algorithms, implement prototypes and possibly prepare manuscript for publication. The internship would be 3-6 months, and the start date is flexible.