MERL is looking for a highly motivated individual to develop planning and control algorithms for multi-agent systems for resource monitoring. The ideal candidate has experience in multi-agent motion planning and data-driven, sequential decision-making. The ideal candidate will have published in one or more of these topics: planning over discrete spaces, statistical estimation and hypothesis testing, reinforcement learning, and planning and control of aerial and ground robots. The candidate should be proficient in Python. Additional knowledge of ROS and C/C++ and demonstrable experience in ground and aerial robots are a plus. The minimum duration of the internship is 3 months; the start time is Summer/Fall 2024.

The Computational Sensing Team at MERL is seeking an intern to work on estimation algorithms for single-photon lidar. The candidate should have experience with statistical modeling and estimation theory. A detailed knowledge of single-photon detection, lidar, and/or Poisson processes is preferred. Hands-on optics experience is beneficial but not required. Strong programming skills in Python or Matlab are essential. Publication of the results produced during our internships is expected. The duration is anticipated to be 3-6 months.

The Computational Sensing Team at MERL is seeking an intern to work on estimation algorithms for radioactive source localization. The candidate should have experience with statistical modeling and estimation theory. A detailed knowledge of interactions of particles with matter, imaging inverse problems, and/or computed tomography is preferred. Hands-on experience with high-energy physics simulators (e.g., Geant4) is beneficial but not required. Strong programming skills in Python are essential. Publication of the results produced during our internships is expected. The duration is anticipated to be 3-6 months.

MERL is seeking a talented researcher to collaborate in the development of design algorithms for metalenses that are freeform, multilayer, and broadband. The ideal applicant will have a strong background in the relevant physics & maths, and has some fluency with the topology optimization and EM simulation tools commonly used in metasurface optics. Also desirable: familiarity with machine learning / AI tools and methods.

MERL is seeking a motivated and qualified intern to conduct research on shape optimization of electrical machines. The ideal candidate should have a solid background and demonstrated research experience in mathematical optimization methods, including topology optimization, robust optimization, and sensitivity analysis, as well as machine learning methods. Hands-on coding experience with the implementation of topology optimization algorithms and finite-element simulation are desirable. Knowledge and experience with electric machine principle, design and finite-element analysis is a strong plus. Senior Ph.D. students in related expertise are encouraged to apply. Start date for this internship is flexible and the duration is 3-6 months.

MERL is seeing a motivated and qualified individual to conduct research on electric motor design and modeling, with a strong focus on electromagnetic analysis. Ideal candidates should be Ph.D. students with solid background and publication record in one more research area on electric machines: electric and magnetic modeling, new machine design and prototyping, harmonic analysis, fault detection, and predictive maintenance. Research experiences on modeling and analysis of electric machines and fault diagnosis are required. Hands-on experience with new motor design and data analysis techniques are highly desirable. Start date for this internship is flexible and the duration is 3-6 months.