[OPEN] [IML_HCI_066] Electromagnetics Modelling and Simulations for Near Field to Far Field Edge Diffraction

Edge diffraction is a newly found phenomenon deviating light locally and producing a dense photonic jet. The goal of the internship is to design and run electromagnetics simulations to compare to internal modelling tools providing micro structures designs. The intern will be part of the Light Field Photonics research team and will participate to innovative optical function design and evaluation

Skills : Understanding of electro-magnetism and diffraction theory. Simulation tools : CST or Comsol, Development skills, Matlab, Visual Studio

Keywords : Nanojet, AR glasses, Diffraction

This internship is located in Rennes, France. If interested, please apply at stage.technicolor@technicolor.com by sending us your resume and a cover letter with the internship reference in the email subject line.

[OPEN] [IML_HCI_053] Modelling Tool for Near Field to Far Field Edge Diffraction

Edge diffraction is a newly found phenomenon deviating light locally and producing a dense photonic jet. The goal of the internship is to implement a simulation tool into Matlab. The tool will be based on building blocks and shall enable to predict near and far field behaviour when combining blocks, in order to realize and study optical functions. Eventually, the tool can be ported to GPGPU using OpenCL. The diffraction will be compared to known electromagnetic simulation packages like CST or Comsol. Investigations on active components will also be pursued

Skills : Understanding of electro-magnetism and diffraction theory. Simulation tools : CST or Comsol, Development skills, Matlab, Visual Studio

Keywords : Nanojet, AR glasses, Diffraction

This internship is located in Rennes, France. If interested, please apply at stage.technicolor@technicolor.com by sending us your resume and a cover letter with the internship reference in the email subject line.

[CLOSED] [ISL_CV_DM_HCI_048] Deep Learning for Gesture Recognition

Gestures are a common form of human communication. Researchers and engineers have thus naturally developed techniques to use this form of communication to interact with computers. The development of new sensors, e.g., Kinect-like sensors, and new deep learning techniques have improved significantly the performance of gesture recognition systems. Nevertheless, these techniques are still demanding in terms of computational resources, and quantity of data for training. Improvements are still needed to make these systems work in complex environment. In this internship, the goal is to explore new deep learning techniques and the use of new type of data, e.g., event-based camera, to reduce computational costs and the quantity of data for training

Skills : A good candidate will have confidence with computer vision, deep learning and the implementation of algorithms using languages such as C/C++ or Python and libraries such as Tensorflow or Theano

Keywords : gesture recognition, hand pose estimation, deep learning

This internship is located in Rennes, France. If interested, please apply at stage.technicolor@technicolor.com by sending us your resume and a cover letter with the internship reference in the email subject line.

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