We are seeking a talented graduate student for a research project in metalens design optimization. Ideal applicants will have a strong mathematics/optimization background, experience with metasurface optics, and fluency in scientific programming. Background knowledge in machine learning, freeform metasurfaces, or polarometry is a plus, as is a working knowledge of any of the following tools: Lumerical, RWCA, ComSol, Matlab, pyTorch/scipy. Creativity and execution are more important than expertise.

MERL is looking for a talented intern to work on the next generation Digital-predistortion algorithms for power amplifier linearization such as 5G. The development of a DPD system involves aspects of signal processing and statistical algorithm design, RF components and instrumentation, digital hardware and software. It is therefore both a challenging and intellectually rewarding experience. This will involve MATLAB coding, interfacing to test equipment such as power sources, signal generators and analyzers and construction and calibration of RF component assemblies. The ideal candidate should have knowledge and experience in adaptive signal processing, machine learning, and radio communication. Good practical laboratory skills are needed. RF semiconductor devices and circuit knowledge is a plus. Duration is 3 to 6 months. This internship requires work that can only be done at MERL.

MERL is seeking an intern to work on estimation algorithms for coherent optical imaging. The ideal candidate would be a senior PhD student working in coherent imaging. The candidate should have experience in statistical modeling and estimation. A detailed knowledge of optical interferometry and imaging with a focus on either optical coherence tomography, optical coherence microscopy or FMCW LIDAR is also preferred. Strong programming skills in MATLAB or Python are essential. Publication of the results produced during our internships is expected. Duration is anticipated to be 3 to 6 months.