We are seeking a highly motivated intern to conduct original research extending MERL's work on uncertainty estimation in face landmark localization (the LUVLi model) to the domains of 3D faces and video sequences. 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. Experience in deep learning-based face landmark estimation, video tracking, and 3D face modeling is preferred. Strong programming skills, experience developing and implementing new models in deep learning platforms such as PyTorch, and broad knowledge of machine learning and deep learning methods are expected.

MERL is looking for a highly motivated and qualified candidate to work on machine learning algorithms for prediction of spatiotemporal data represented as time series of geospatial locations. The ideal candidate will have solid understanding of sequence prediction algorithms, including transformer neural networks, recurrent neural networks, and other deep neural network models, as well as good foundational knowledge of discrete event systems, including Markov and semi-Markov models. Demonstrated hands-on experience with PyTorch or other Python implementations of such algorithms is required. Additional knowledge of time series analysis and statistical machine learning, as well as experience with tools and methods for geospatial processing would be a plus. PhD students are preferred, but Master's students will be considered, too. The expected duration of the internship 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.

MERL is seeking a highly motivated candidate for development of next-generation high-precision positioning methods for autonomous systems applications, e.g., autonomous driving. The candidate will work with the Control for Autonomy team and the Signal Processing group in developing satellite-based positioning methods using information from multiple sources. Previous experience with at least some of the Bayesian inference, distributed estimation, satellite navigation systems, is highly desirable. Solid knowledge in MATLAB is required, working experience in C/C++ is desired, and previous experience with satellite navigation packages such as RTKLib is a merit. PhD candidates meeting the above requirements are encouraged to apply. The expected duration of the internship is 3-6 months with flexible start date. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.