The Signal Processing (SP) group at MERL is seeking a highly motivated intern to conduct fundamental research in wireless sensing using communication signals such as 5G, WiFi, and Bluetooth. Previous experience on occupancy sensing, people counting, localization, device-free pose/gesture recognition with machine learning approaches is highly preferred. Familiarity with IEEE 802.11 (ac/ad/ay)standards is a plus. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, collect real-world channel measurements, and prepare results for publication. Senior Ph.D. students with research focuses on wireless communications, machine learning, signal processing, optimization, applied mathematics, or related areas are encouraged to apply. The expected duration of the internship is 3 months with a flexible start date.

The Computational Sensing team at MERL is seeking motivated and qualified individuals to develop algorithms that solve inverse problems in computational sensing that incorporate deep learning architectures for a variety of sensing applications. The project goal is to improve the performance and develop an analysis of algorithms used for inverse problems by incorporating new tools from machine learning and artificial intelligence. Ideal candidates should be Ph.D. students and have solid background and publication record in any of the following, or related areas: imaging inverse problems, large-scale optimization, plug-and-play priors, learning-based modeling for imaging, learning theory for computational imaging, and Koopman theory/dynamic mode decomposition. Publication of the results produced during our internships is expected. The duration of the internships is anticipated to be 3-6 months. Start date is flexible.

The Signal Processing (SP) group at MERL is seeking a highly motivated intern to conduct fundamental research in automotive radar-based object tracking and perception for autonomous driving. Previous experience on multiple (point and extended) object tracking, data association, and data-driven object detection/tracking is highly preferred. Knowledge about automotive radar schemes (MIMO array and waveform modulation (FMCW, PMCW, and OFDM)) and hands-on experience on open automotive datasets are a plus. Knowledge on vehicle dynamics is an asset. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, conduct field measurements, data analysis (Python & MATLAB), and prepare results for patents and publication. Senior Ph.D. students with research focuses on signal processing, machine learning, optimization, applied mathematics, or related areas are encouraged to apply. The expected duration of the internship is 3 months with a flexible start date.

MERL is seeking a motivated and qualified intern to conduct research in the design, modeling and optimization of electrical machines. The ideal candidate should have solid backgrounds in electromagnetic theory, electric machine design, and numerical modeling techniques (including model reduction), research experiences in electric, magnetic, and thermal modeling and analysis of electrical machines, and demonstrated capability to publish results in leading conferences/journals. Experience with ANSYS, COMSOL, and optimization techniques is a strong plus. Senior Ph.D. students in electrical or mechanical engineering with related expertise are encouraged to apply. Start date for this internship is flexible and the duration is 3-6 months.

MERL is seeking a motivated and qualified individual to conduct research on dynamics and control of electric vehicles. The ideal candidate should have a solid background in vehicle dynamics and control. PhD students in mechanical or electrical engineering with focus on automotive systems are encouraged to apply. The intern will collaborate with MERL researchers in developing nonlinear control algorithms for electric vehicles. Start date for this internship is flexible and the expected duration is approximately 3-6 months.

The Control and Dynamical Systems (CD) group at MERL is seeking a highly motivated intern to work on hybrid model predictive control. The scope of work includes the development of model predictive control algorithms for hybrid dynamical systems, switched systems, and quantized systems, analysis and property proving, and applications in automotive, space systems, and energy systems. PhD students with expertise in some among control, optimization, model predictive control and hybrid systems, and with working knowledge of Matlab implementation are welcome to apply. The expected duration of the internship is 3-6 months with flexible start date.

MERL is seeking a highly skilled and self-motivated intern to conduct research on condition monitoring for robotic manipulators. The ideal candidate should have solid backgrounds in robotic manipulators, stochastic estimation methods for dynamical systems, and collaborative strategies over multi-agents. Experience of applying machine learning to dynamical systems is a strong plus. Excellent coding skill and strong publication records are necessary. Senior Ph.D. students in control, robotics, or related areas are encouraged to apply. Start date for this internship is flexible, and the expected duration is about 3 months.

MERL is looking for highly motivated individuals to work on efficient numerical algorithms and applications of mixed-integer optimal control methods. The research will involve some among the following: the study and development of mixed-integer optimization techniques for optimal control, the implementation and validation of algorithms for relevant control applications. The ideal candidate should have experience in branch-and-bound methods and presolve techniques for mixed-integer optimization and/or model predictive control. PhD students in engineering or mathematics with a focus on mixed-integer optimization or numerical optimal control are encouraged to apply. Publication of relevant results in conference proceedings and journals is expected. Capability of implementing the designs and algorithms in Matlab is expected; coding parts of the algorithms in C/C++ is a big plus. The expected duration of the internship is 3-6 months and the start date is flexible.

The Control and Dynamical Systems (CD) group at MERL is seeking a highly motivated intern for a research position in control of space vehicles. The ideal candidate is working towards a Ph.D. in aerospace, mechanical, or electrical engineering, and has background in both optimization-based control and space vehicle dynamics. The candidate is expected to possess strong abilities in algorithm analysis and Matlab implementation. The duration of the internship is approximately 3 months. Publication of results produced during the internship is expected.