Characterization of Electrical Properties of Electrolytes: Combining AI-Powered Microfluidics w[...]

Organisation/Company IFP Energies nouvelles (IFPEN) Research Field Chemistry » Physical chemistry Engineering » Chemical engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 31 Jul 2026 - 14:01 (Africa/Abidjan) Country France Type of Contract Temporary Job Status Full-time Hours Per Week 36 Offer Starting Date 2 Nov 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

The global energy transition towards the electrification of transportation has triggered an unprecedented demand in battery production, leading to a critical need for the sustainable recovery of strategic metals such as lithium or cobalt. While hydrometallurgy is a highly effective recycling pathway, it remains resource-intensive and environmentally challenging due to high toxic reagent consumption. Optimizing these processes requires a deep understanding of the solvents involved, such as highly concentrated aqueous or non-aqueous electrolytes.

Accurate modeling of these systems relies specifically on the knowledge of electrical conductivity, which is a measure of ion dissociation along with dielectric permittivity, which governs electrostatic interactions and ion solvation. Traditional "trial-and-error" experimental methods are no longer sufficient to explore the vast chemical space of recycling streams. To overcome this, "AI-Powered Microfluidics" - the synergy between microscale fluidics and Artificial Intelligence - offers a powerful high-throughput platform to generate the high-quality experimental data needed for advanced thermodynamic modeling. This PhD project aims to bridge the gap between experimental data high throughput generation and predictive modeling by measuring the conductivity and permittivity of diverse electrolytes. The research will be structured into four key phases: (i) the design, fabrication, and validation of innovative microfluidic chips; (ii) the coupling of these microchips with in situ analytical techniques; (iii) the AI-optimized acquisition of experimental data; and (iv) the refinement of thermodynamic models. This thesis will specifically contribute to the development of the predictive ePPC-SAFT equation of state (in coordination with another PhD project) enabling an explicit description of ionic solvation and complexation. Furthermore, it offers the opportunity to work at the interface of chemical engineering, thermodynamics, and data science, to address challenges in the circular economy

Where to apply

E-mail

Requirements

Research Field Chemistry » Physical chemistry Education Level Master Degree or equivalent

Research Field Engineering » Chemical engineering Education Level Master Degree or equivalent

Specific Requirements

Rigorous scientific attitude and collaborative spirit