PhD position at IFPEN in Mechanical Engineering: Development of an AI-based strategy to acceler[...]

IFP Energies nouvelles (IFPEN); France; Sur place; EUR 40 000 - 60 000; Plein temps

Résumé du poste

A research and innovation center in France is seeking a PhD researcher to focus on the development and optimization of gas turbines using Sustainable Aviation Fuels. The candidate will utilize machine learning techniques to accelerate chemical kinetics computations in multiphase combustion scenarios. Ideal candidates will have a Master's degree in a related field and excellent English skills. This is a full-time position with a deadline for applications on 30 Sep 2026 and competitive salary and benefits.

Prestations

Access to first-class laboratory infrastructures

Competitive salary and benefits

Dedicated seminars and training sessions

Qualifications

• Excellent command of English required for this role.
• Experience with machine learning and chemical kinetics preferable.

Responsabilités

• Extend machine learning-based chemical kinetics acceleration methods.
• Generate a suitable training dataset reflecting industrial operating conditions.
• Focus on multiphase combustion of Sustainable Aviation Fuels.

Connaissances

Machine learning techniques

Numerical simulation

Chemical kinetics

Computational efficiency

Understanding of multiphase combustion

Formation

University Master degree involving CFD, physics and/or numerical modelling

Description du poste

Organisation/Company IFP Energies nouvelles (IFPEN) Research Field Engineering » Mechanical engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 30 Sep 2026 - 23:59 (Europe/Paris) Country France Type of Contract Temporary Job Status Full-time Hours Per Week 35 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

Air traffic is responsible for an increasingly significant share of global CO₂ emissions. The use of Sustainable Aviation Fuels (SAFs) offers a promising path toward reducing the carbon footprint of the aviation sector. Although SAFs exhibit physico-chemical properties similar to conventional kerosene, their combustion behavior can differ significantly, requiring adjustments and optimization of current gas turbines (GT). In this context, numerical simulation plays an essential role for the aerospace industry, enabling cost-effective design and optimization of GT systems. However, several challenges remain, particularly in achieving both accurate and computationally efficient simulations. One of the main bottlenecks is the numerical integration of chemical kinetics, which is computationally expensive due to the large number of species and reactions involved in SAF combustion. Recent advances have demonstrated that machine learning techniques, particularly neural networks, can significantly accelerate chemical kinetics computations. Nevertheless, most of these developments have focused on conventional hydrocarbons under purely gaseous conditions. In contrast, SAF combustion in GTs occurs in a multiphase regime, where complex interactions between liquid fuel droplets and the flame must be taken into account. The objective of this thesis is to extend machine learning‑based chemical kinetics acceleration methods to the multiphase combustion of SAFs injected as sprays. A key challenge lies in generating a suitable training dataset that accurately reflects the operating conditions of industrial systems. The work will build upon a methodology previously developed at CORIA and IFPEN, based on the coupled simulation of interacting 0D reactors, which will be adapted to account for spray combustion dynamics. The research will initially focus on a canonical laboratory flame, before being applied to a configuration representative of an actual SAF-fueled aero‑engine burner.

Keywords: Sustainable Aviation Fuel, Artificial Intelligence, Chemical kinetics, Computing acceleration

Academic supervisor: Pr Luc VERVISCH, CORIA, ORCID: 0000-0003-0313-2060

University Master degree involving CFD, physics and/or numerical modelling

Specific Requirements

Languages ENGLISH Level Excellent

Additional Information

IFP Energies nouvelles is a French public-sector research, innovation and training center. Its mission is to develop efficient, economical, clean and sustainable technologies in the fields of energy, transport and the environment.

IFPEN offers a stimulating research environment, with access to first in class laboratory infrastructures and computing facilities. IFPEN offers competitive salary and benefits packages. All PhD students have access to dedicated seminars and training sessions.