The internship program at MERL emphasizes close collaboration with a particular researcher or members of a small team. Primary areas of work include:
- Mechatronics and control
- Algorithms and optimization
- Signal Processing
- Digital & optical communications
- Imaging, computer vision and graphics
- Video processing
- Data Analysis, modeling and planning systems
- Information security
- Speech and audio processing
- Machine learning
A primary intent of the program is to provide interns with experience that could help them enhance and accelerate their professional career, while also contributing to new or ongoing initiatives at MERL. Interns will be exposed to relevant industrial problems ranging from speculative and exploratory research to more practical engineering tasks. We hope that all interns have a chance to become familiar with our organization and the open research culture at MERL, produce publishable work, and develop an appreciation for how breakthrough research makes an impact on future products.
MERL considers graduate students from all over the world. As many of our projects benefit from specialized knowledge in a given field, graduate students pursuing a Ph.D. typically fill the majority of internship openings.
The duration of a typical internship varies from 3 months to 1 year, with the majority of interns being employed during the summer months. As the summer is a very busy time, we encourage applications for non-summer internships and also prefer early applications for summer internships. Hiring decisions for the summer are typically made around February/March to allow enough time for any necessary paperwork (such as visa applications or other work eligibility forms) to be completed.
Boston is a fantastic city with one of the largest student populations of any city in the US. There are many social and leisure activities to get involved in, and no shortage of things to do during your stay. With close proximity to major universities such as MIT and Harvard, there are also many chances to attend seminars and lectures by well-known experts in various fields. During the summer, MERL also organizes a number of social activities for all interns to participate. Some past activities included BBQ outings, whale watching, Wii tournaments, and movie nights.
- Experience: At MERL, you work closely with top researchers and participate in a lab-wide R&D culture with a unique mix of curiosity-driven research and market-oriented prototyping.
- Publication: MERL is an open research lab with a strong tradition of publication in high-impact peer-reviewed venues. Internships typically aim at producing publication-worthy results and interns are co-authors on many papers each year.
- Compensation: MERL offers competitive salaries based on relevant education, skills, and work experience.
- Perks: MERL provides relocation assistance including travel costs; subsidies for commuting costs; and entertainment events for interns to get to know Boston and each other.
- Networking: Interns are encouraged to network with MERL's research staff, fellow interns, and faculty at local universities. Weekly socials and seminars provide many opportunities.
- Opportunity: Many MERL interns have gone on to distinguished careers at MERL. MERL research hosts have often provided letters of reference supporting their ex-interns' candidacies for jobs, fellowships, and tenure.
Publications are an important output of MERL's research, and internships often lead to one or more publications. Below is a sample listing of some recent publications that include interns as co-authors. Please visit our publications page for a complete listing of MERL papers.
- "Sparse sensing and DMD based identification of flow regimes and bifurcations in complex flows", SIAM Journal on Applied Dynamical Systems, DOI: 10.1137/15M104565X, July 2017. ,
- "Crowd Flow Completion From Partial Spatial Observations Using Kernel DMD", International Conference on Sampling Theory and Applications (SampTA), July 2017. ,
- "Deep Active Learning for Civil Infrastructure Defect Detection and Classification", International Workshop on Computing in Civil Engineering (IWCCE), June 2017. ,
- "A Novel BESS-based Fast Synchronization Method for Power Grids", IEEE PES PowerTech, June 2017. ,
- "Online Convolutional Dictionary Learning for Multimodal Imaging", arXiv, June 2017. ,
- "Sampling-based Algorithms for Optimal Motion Planning Using Closed-loop Prediction", IEEE International Conference on Robotics and Automation (ICRA), May 2017. ,
- "MonoRGBD-SLAM: Simultaneous Localization and Mapping Using Both Monocular and RGBD Cameras", IEEE International Conference on Robotics and Automation (ICRA), May 2017. ,
- "ROS2D: Image Feature Detector Using Rank Order Statistics", IEEE International Conference on Robotics and Automation (ICRA), May 2017. ,
- "Soft Video Delivery for Free Viewpoint Video", IEEE International Conference on Communications (ICC), May 2017. ,
- "A Neuro-Adaptive Architecture for Extremum Seeking Control Using Hybrid Learning Dynamics", American Control Conference (ACC), April 2017. ,
- "Compressive Imaging with Iterative Forward Models", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2017. ,
- "Deep Clustering and Conventional Networks for Music Separation: Strong Together", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2017. ,
- "Joint CTC- Attention Based End-to-End Speech Recognition Using Multi-task Learning", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2017. ,
- "Disc-Glasso: Discriminative Graph Learning with Sparsity Regularization", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2017. ,
- "On optimal performance of nonlinear energy sinks in multiple-degree-of-freedom systems", Journal of Sound and Vibration, DOI: 10.1016/j.jsv.2016.10.025, Vol. 388, pp. 272-297, February 2017. ,