Post-doctoral positions in Complex Nanophotonics and Novel Lasers

Yale University, Department of Applied Physics

Two post‐doctoral positions are immediately available in Professor Hui Cao's lab at Applied Physics Department of Yale University for experimental research on complex nanophotonic devices and novel lasers. The goal of this research is to harness optical nonlinearities in complex systems for photonic applications. Responsibilities include designing and fabricating photonic devices, building an optical setup, performing the experiments, and the data analysis. Familiarity with nanofabrication, numerical modeling, and wavefront shaping techniques is a plus. More information about Cao’s research program can be found at the website.

Please send your academic CV, one publication, and names/email addresses of three references to hui.cao@yale.edu.

Time reversed optical waves by arbitrary vector spatiotemporal field generation

[M. Mounaix et al., Nat. Commun., 11 (2020)]

Time-reversal allows precisely tailoring the spatio-temporal field and was originally demonstrated in acoustics. Time-reversal requires to temporally modulate the optical field independently over a large number of pixels, which is challenging in optical experiments. In the present paper, the authors developed a system allowing the modulation of the optical field spectrally and spatially over a 2d array. Harnessing this new tool, they perform a time-reversal experiment to focus and shape the optical field temporally and spatially through a multimode fiber.

Image reconstruction through unknown random configurations of multimode fibers using deep learning

[S. Resisi et al, arxiv, 2011.05144 (2020)]

Multimode fibers tend to scramble input images due to intermodal dispersion and random mode coupling. While this scrambling effect can be learned, for instance by measuring the transmission matrix of the fiber, slight changes of the geometrical conformation of the fiber modify its response, making the calibration obsolete. In the present paper, the authors use deep-learning using data sets acquired over a wide range of deformations to reconstruct images sent through unknown configurations of multimode fibers. This is possible thanks to the presence of invariant properties that the numerical model learns.

Fast numerical estimations of axisymmetric multimode fibers modes