MERL is seeking a highly motivated intern to conduct original research in data-driven realistic image generation that learns and combines implicit and explicit deep 3D models from unlabeled images. 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 (or postdoc) in computer vision with experience in implicit neural network models, GANs, and related deep learning methods, as well as good general knowledge in machine learning and a strong publication record. Previous experience with 3D face models and video generation is preferred. Strong programming skills in Python and flexibility working across various deep learning platforms (e.g., PyTorch and TensorFlow) are expected.

MERL is looking for a self-motivated intern to work on problems at the intersection of video understanding and graph representation learning for solving few-shot action recognition problems. The ideal candidate would be a PhD student with a strong mathematical background in machine learning and computer vision and who has published at least one paper in a top-tier machine learning or computer vision venue (NIPS/CVPR/ECCV/ICCV/ICML/PAMI etc.). The candidate must have prior experience in using deep learning methods for video understanding (such as using Transformers) and self/unsupervised methods (such as contrastive learning). Proficiency in PyTorch is expected and familiarity with neural language models will be a plus. The intern will conduct original research with MERL researchers towards scientific publications. This internship requires work that can only be done at MERL.

MERL is looking for a highly motivated individual to work on safe control of data-driven, uncertain, dynamical systems. The research will develop novel optimization and learning-based control algorithms to guarantee safety and performance in various industrial applications, including autonomous driving. The ideal candidate should have experience in either one or multiple of the following topics: optimal control under uncertainty, (robust and stochastic) model predictive control, (convex and non-convex) optimization, and (reinforcement and statistical) learning. Ph.D. students in engineering or mathematics with a focus on control, optimization, and learning are encouraged to apply. A successful internship will result in submission of relevant results to peer-reviewed conference proceedings and journals, and development of well-documented (Python/MATLAB) code for MERL. The expected duration of the internship is 3-6 months, and the start date is Summer 2022.