Scientific project
Technologies exploiting solar and thermal energies are promising avenues that could help realize sustainable and alternative energy sources.
However, it is essential to find suitable materials and then evaluate their performance by simulating them from the material to the device level, providing a quick and inexpensive way to verify device designs and processes. Topological insulators (TIs), possess novel symmetry-protected electronic and optical properties that make them promising candidates as future highly efficient quantum
materials for energy conversion [1]. By exploiting first principles simulation techniques from theoretical physics and chemistry, this project aims to understand the correlation between the topology of electrons/phonons, low dimensionality of materials and their applications in the field of thermoelectricity (i.e. that is to say the direct conversion of heat flow into electricity) and propose new interesting materials.
In fact, TIs exhibit intrinsic properties that are “topologically protected” [2], allowing electrons not to suffer from backscattering due to impurities and defects (unlike phonons). This allows for an effective decoupling of the two types of transport [3] and thus an independent means for simultaneous optimization of electronic and thermal conductivity, which can also be improved by reducing the dimensionality of the system [4].
[1] K. Behnia, “Fundamentals of Thermoelectricity” (Oxford University Press, 2015
[2] N. Xu, et al., npj Quantum Materials 2, 51 (2017
[3] K. Pal, S. Anand, and U. V. Waghmare, J. Mater. Chem. C 3, 12130 (2015)
[4] Y. Ichinose, et al., Phys. Rev. Mat. 5, 025404 (2021)

                                               For more information and to apply for this vacancy : see attached file

We are seeking a postdoctoral researcher with interest and experience in integrated photonics to join the Silicon Photonics team at the Center for Nanoscience and Nanotechnology (C2N). This project stems from a longl asting collaboration with the National Research Council Canada (NRC) and aims at developing accurate silicon photonics surrogate models using machine learning. In particular, we will address the key open challenge of modeling the impact of fabrication and operation environment on the perfromance of photonic devices. We will exploit both simulations and optical measurements in a data-efficient manner, and leverage transfer learning and active learning methods. The developed models will then be used to anticipate and pre-emptively compensate for fabrication errors, providing an innovative approach to design and calibrate large photonic circuits

The successful candidate will be responsible for the design and layout of photonic components and circuits to be used as test cases; fabrication of the devices in the C2N clean room and their experimental characterization to build the dataset required for neural network training; identify, implement, and train neural network architectures both for optical behavior prediction and inverse design purposes. Activities will be carried out in collaboration with the PI and the NRC team. Short stays in Ottawa (Canada) are foreseen as part of the project.