PhD position in engineering on the development of state-to-state plasma-assisted combustion solvers

Vrije Universiteit Brussel; Belgique; On-site; EUR 22,000 - 31,000; Full time

Benefits offered by this job

Home-work commute reimbursement

Group insurance & guaranteed income

Learning & development opportunities

Sport facilities

Campus restaurants

35 days of leave

Job summary

Vrije Universiteit Brussel seeks a PhD researcher to join the ERC Starting Grant project ESCAPADE, focusing on plasma-assisted combustion with advanced reduced-order modelling. The candidate will build high-fidelity, reduced plasma-chemistry models integrated into CFD environments for ammonia/hydrogen combustion systems.

The project combines state-of-the-art kinetic modelling, machine learning, and mechanism reduction to enable efficient simulations from DNS to LES under diverse operating

Qualifications

• Master's degree in Engineering, Applied Mathematics, Physics, Computational Science or related field.
• Strong background in numerical methods and scientific computing.
• Excellent programming skills in Python, Fortran and/or C++.
• Interest in chemical kinetics, plasma physics, combustion, reduced-order modelling and machine learning.

Skills

Numerical methods

Scientific computing

Programming (Python)

Programming (Fortran)

Programming (C++)

Machine learning

Education

Master of Science in Engineering or related field

Tools

Python

Fortran

C++

Job description

Organisation/Company Vrije Universiteit Brussel Department Mechanical Engineering Research Field Engineering » Other Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 16 Nov 2026 - 10:00 (Europe/Brussels) Country Belgium Type of Contract Temporary Job Status Full-time Offer Starting Date 1 Feb 2027 Is the job funded through the EU Research Framework Programme? Horizon Europe - ERC Reference Number 101302707 Is the Job related to staff position within a Research Infrastructure? No

Offer Description

We offer a full-time PhD position in the framework of the ERC Starting Grant project “Energising the future with clean plasma-based technologies” (ESCAPADE, proposal number 101302707).

Plasma-assisted combustion combines non-equilibrium plasma discharges with combustion processes to improve ignition, flame stability, and pollutant emissions for sustainable fuels. While detailed state-to-state plasma-combustion models provide an unprecedented description of the underlying physics, their computational cost remains prohibitively high for multidimensional simulations and industrial applications.

This PhD project aims to develop the next generation of computational tools for plasma-assisted combustion by combining detailed state-to-state kinetic modelling, mechanism reduction, and physics-informed machine learning. The researcher will contribute to the development of advanced plasma-assisted combustion simulation codes capable of explicitly modelling plasma discharges, non-equilibrium chemistry, and combustion processes across a wide range of operating conditions.

A key objective of the project is the development of novel reduction methodologies for large state-to-state kinetic mechanisms containing hundreds to thousands of species and reactions. Building upon recent advances such as plasma-directed relation graph techniques (P-DRGEP), principal component analysis, and machine-learning-based lumping strategies, the PhD candidate will construct high-fidelity reduced-order models that preserve the essential plasma-combustion interactions while significantly reducing computational costs.

The developed methodologies will be integrated into in-house plasma-assisted combustion solvers and validated against experimental benchmark datasets generated by leading international partners of the project. Emphasis will be placed on ammonia/hydrogen combustion systems, which are emerging as key carbon-free fuels for future energy and propulsion applications. The resulting reduced models will enable direct integration of detailed plasma chemistry into advanced CFD environments, including DNS and LES simulations of realistic combustion systems.

References:

-Rekkas-Ventiris, G., Giuntini, L., Sabia, P., Sorrentino, G., & Bellemans, A. (2026). Numerical investigation of plasma-assisted MILD combustion of ammonia using a hybrid modeling approach. Fuel , 406 , 136751.

-Rekkas-Ventiris, G., Gomez, A. D., Deak, N., Kincaid, N., Pepiot, P., Bisetti, F., & Bellemans, A. (2024). A novel machine learning based lumping approach for the reduction of large kinetic mechanisms for plasma-assisted combustion applications. Combustion and Flame , 260 , 113252.

• A Master of Science in Engineering, Applied Mathematics, Physics, Computational Science or a related field.
• Strong background in numerical methods and scientific computing.
• Excellent programming skills (Python, Fortran and/or C++).
• Interest in chemical kinetics, plasma physics, combustion, reduced-order modelling, and machine learning.

Specific Requirements

• Experience with CFD, DNS and/or LES methodologies is considered an asset.
• Experience with kinetic mechanism reduction techniques, data-driven modelling or high-performance computing is considered an asset.
• Fluent in written and spoken English.
• Strong analytical skills and ability to work both independently and as part of an international research team.

Languages ENGLISH Level Excellent

Additional Information

Are you going to be our new colleague? You'll be offered a full-time contract with a competitive salary according to your competences and previous relevant work experience. At the VUB, you're guaranteed an open, inclusive and diverse workplace where you are offered opportunities to (further) build on your career.

In addition, you'll enjoy various benefits:

• Full reimbursement for your home-work commute with public transport‚ according to VUB-policy, or compensation if you come by bike;
• Cost-free hospitalization and group insurance with guaranteed income;
• The space to form your job content and‚ continuously learn‚ via VUB LRN;
• Excellent facilities for sport and exercise;
• Delicious meals at attractive prices in our‚ campus restaurants;
• An open, family-friendly work environment where attention is paid to work-life balance, and exceptional holiday arrangements with‚ 35 days of leave‚ (based on a full-time contract).