IFPEN’s doctoral thesis positions for fall 2025 are now available!
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The simulation of reactive transport in porous media is a major challenge for many IFPEN projects, especially in relation to new underground energies such as CO2 sequestration, geothermal energy and hydrogen storage.
The electric vehicle market is today a fast growing market. This induces the development of characterization techniques for the study of batteries materials.
Economical, geopolitical and social trends, apart from the well-posed environmental concerns, are possible to precipitate legislative actions for the partial substitution of Diesel by cleaner fuels in the imminent future. Besides, the already stringiest emission legislation referring to Diesel engines, e.g.
Permanent magnet synchronous machines are frequently used in transport electrification applications because of their high efficiency. The control of these machines requires knowing with precision the speed and the position of the rotor. Position sensors, habitually used, are efficient but present some inconvenient.
In order to gain more insight into physical processes, IFPEN develops or uses a large number of numerical simulators. These numerical models are useful to reproduce phenomena in specific conditions and thus to predict and explore the system response under various conditions.
This project is conducted thanks to a collaboration between two laboratories from University of Lorraine (LCPME) and IFPEN. It aims at better quantifying biocide efficiency for geothermal application.
For several years now, plans to deploy hydrogen to decarbonize the economy have been multiplying around the world.
Wind farm flows are complex and difficult to predict, although their understanding is crucial for proper wind farm design and layout optimization. During the design phase, so-called “engineering” flow models, whose purpose is to provide an analytical representation of wind turbine wakes, are used to estimate wind farm power production.
Numerical simulation is a strategic tool since it is indispensable for research and many industrial applications that require multiphase flow resolution.
The purpose of this doctoral project is to broaden the understanding of the conditions for a massive deployment of emerging CDR technologies in a market environment.
Les méthodes d'apprentissage basées sur les architectures neuronales permettent de résoudre des problèmes de classification et de segmentation avec des performances pour l'instant non égalées dans le domaine du traitement d'images et de la vision par ordinateur.
IFPEN and the Utrecht University call for qualified candidates for a PhD project, entitled: “Fluid-Flow and Fluid-Rock Interactions during the Dinarides collisional orogenesis: Implications on geo-resources and sustainable development.” This project is based on the previously acquired field-knowledge and numerical models, which will be the start