Scientific direction Development of key enabling technologies
Transfer of knowledge to industry

PostDocs : selection by topics

Engineering science >> Mathematics - Numerical analysis - Simulation
3 proposition(s).

Sizing and control optimisation of a hydrogen production system coupled with an offshore wind farm





Coupling MRE (Marine Renewable Energy) and hydrogen sectors reveal an important potential long-term assets. The MHyWind project suggests to estimate the energetic and economic potential of a hydrogen production system integrated into a substation of an offshore wind farm. The hydrogen produced and stored locally will be distributed by boat for harbour uses, as a replacement of fossil fuels. For that purpose, it will be organized a simulation which will integrate all the energy chain towards the harbour uses of hydrogen. It will allow to estimate various configurations and sizing according to the local uses, valuation leverages, control modes and behavior of the system. The criteria will be the producible (kg of H2 producted and used) and complet costs (CAPEX and OPEX). The objective of the postdoctoral student will be to develop the simulation tool on this applicative being fully integrated with the teams of concerned laboratories.

PET/MRI nonparametric bayesian bootstrap reconstruction


Laboratoire Modélisation et Simulation des Systèmes



Very recently, Bayesian statistical researchers have suggested an alternative way to learn from large datasets. First, they move away from the conventional framework of Bayesian inference by replacing the generative model with a nonparametric prior directly related to the data distribution. The parameters are then inferred by minimizing a bunch of randomized costs functions. As part of this 18-month post-doctoral fellowship (Physicancer project, led by IGR), we propose to assess the problem of PET/MRI reconstruction in this Bayesian nonparametric learning framework. The study covers the performance assessment of the approach on realistic synthetic dataset by comparison to conventional techniques. Transfer to the Castor environment will be considered in collaboration with researchers of SHFJ. In addition, an analysis of the asymptotic properties of this estimator (concentration of the posterior distribution) will be carried in collaboration with the Laboratoire de Mathématiques d'Orsay (LMO).

Shape optimization for optical computation

Département d'Optronique (LETI)

Laboratoire des Capteurs Optiques



Context CEA - LETI is one of the Europe's leading research centers in microelectronics. This post-doctoral position is proposed within the framework of the CLEAR CARNOT project, involving two departments of CEA - LETI. Namely, the DOPT department specializes in the design, manufacture and characterization of optoelectronic components while the DACLE department performs research on embedded systems and innovative computational architectures. Research topic / Missions Although several extremely compact components performing more or less complex unitary functions have been designed and manufactured in recent years, no practical application of numerical shape optimization methods has emerged to date in the field of integrated photonics. Building on the recent development of optical computation, particularly in the wake of neural network and machine learning, we aim to demonstrate the feasibility (design, fabrication and test) and the applicative interest of integrated optical calculation circuits obtained by shape optimization techniques. The candidate will participate in the choice of the optical computation prototype architecture, and will be in charge of the photonic circuit design (conventional circuit, shape optimized circuit, and finally reconfigurable circuit). He/she will rely on an existing toolbox, dedicated to shape optimization of photonic components, developed as part of an ongoing work. The work should lead to theoretical developments as well as applications, with publications in international journals. Required skills Candidates will have completed a PhD in applied mathematics, mathematical physics or related fields. He/she should demonstrate both theoretical and computational skills. Implementations will be performed in the MATLAB language. Knowledge in shape optimization and an interest for photonics would be greatly appreciated.

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