Contract type: Internship
Starting date: ASAP (2021)
Duration: 3 months
Working Place: Palaiseau
Education: M1 ; M2 ; Engineer
Salary: Salary grid
Become an actor of the Energy Transition by joining a team driven by innovation and impact to address today’s most decisive challenges.
IPVF – Institut Photovoltaïque d’Île-de-France, is a global Research, Innovation and Education center, which mission is to accelerate energy transition through science & technology.
Gathering industrial PV leaders (EDF, Total, Air Liquide, Horiba and Riber) and world-renowned academic research teams (CNRS, Ecole Polytechnique), multi-disciplinary and international IPVF teams conduct research for clean energy technologies.
Supported by the French State, IPVF is labelled Institute for Energy Transition (ITE).
IPVF at a glance:
The IPVF characterization team aims at developping new characterization techniques based on luminescence imaging to extract key properties of the photovoltaic materials. In that frame, we are building the first 4D photoluminescence-imaging set-up, which provides the local spectral and temporal information of the light emitted by semiconductors. It employs Single Pixel Imaging as an alternative to raster scanning to reconstruct the spatial information from a series of measurements performed by a non-imaging detector. A matrix of micro mirrors decomposes the light spatially and select a different area to be analyzed at each measure. Many different approaches can be used to decompose and reconstruct the image. Implementing a compressed sensing algorithm would allow acquisitions with less measurements yet the same spatial resolution. This is crucial in order to reduce the acquisition time of our novel set-up without reducing its spatial resolution and signal-to-noise ratio.
Duarte, M. F. et al. Single-pixel imaging via compressive sampling. IEEE Signal Process. Mag. 25, 83–91 (2008).
Rousset, F. Single-pixel Imaging: development and applications of adaptive methods. (2017).
Bian, L., Suo, J., Dai, Q. & Chen, F. Experimental comparison of single-pixel imaging algorithms. J. Opt. Soc. Am. A 35, 78 (2018).Rousset, F. et al. Adaptive Basis Scan by Wavelet Prediction for Single-Pixel Imaging. IEEE Trans. Comput. Imaging 3, 36–46 (2017).
Yao, R., Ochoa, M., Yan, P. & Intes, X. Net-FLICS: fast quantitative wide-field fluorescence lifetime imaging with compressed sensing – a deep learning approach. Light Sci Appl 8, 26 (2019).
Yu, W.-K. Super sub-Nyquist single-pixel imaging by means of cake-cutting Hadamard basis sort. Sensors 19, 4122 (2019).
IPVF Research Program involving this internship position: https://ipvf.fr/jean-paul-kleider-philip-schulz-and-daniel-ory-introducing-programme-4-characterization-modeling-reliability/
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