FairCFD

This is much more than just a PhD position!

Within the FairCFD Doctoral Network, you will benefit from a unique three-fold experience:

– Contribute to technological innovation in the field of automotive, in direct collaboration with an industrial partner (TME), by developing advanced and efficient CFD strategies.

– Take part in a network-wide interdisciplinary effort to define and promote numerical sustainability in scientific research.

– Join a vibrant network of 15 doctoral candidates, across 9 European countries, with access to cutting-edge network events, high-level training to technical and transverse skills, and secondments in both academic and industrial environments.

See our website for more details on the network’s philosophy and actions, along with description of the other PhD positions (https://www.imft.fr/faircfd/project-presentation/).

 Scientific program

Background

Proton Exchange Membrane Fuel Cells (PEMFCs) are among the most promising technologies for powering vehicles in the transition to cleaner transportation. Unlike conventional internal combustion engines that rely on fossil fuel combustion, PEMFCs generate electricity through an electrochemical reaction between hydrogen and oxygen, with water and heat as the only by-products. This makes them highly attractive for reducing greenhouse gas emissions and improving urban air quality. This PhD will be enrolled in the doctoral program of the National Technical University of Athens (NTUA), Greece, and hosted by the industrial partner TME (Toyota Motor Europe). The research will focus on analysing and optimising PEMFC components, particularly the catalyst and gas diffusion layers, to capture complex physical phenomena while lowering simulation costs through extensive use of Machine Learning models trained on high-fidelity numerical data. Both gradient-free and gradient-based optimisation methods will be employed.

Selected references:

• Monfaredi, E. Papoutsis-Kiachagias, V. Asouti, K. Giannakoglou. Analysis and Optimization of PEM Fuel Cells using OpenFOAM. EUROGEN 2023, 15th International Conference on Evolutionary and Deterministic Methods for Design, Optimization and Control, Chania, Greece, June 1-3 2023.
• Yuan, Y. Tang, M. Pan, Z. Li, B. Tang. Model prediction of effects of operating parameters on proton exchange membrane fuel cell performance. Renewable Energy 2010; 5(3):656 – 666.
• d’Adamo, M. Haslinger, G.E Corda, J. Höflinger, S. Fontanesi, T. Lauer. Modelling methods and validation techniques for CFD simulations of PEM Fuel Cells. Processes 2021; 9(4):688.

Your research program

The research project will initiate from an existing 3D CFD fuel-cell model and software and its adjoint (programmed into the OpenFOAM environment) along with any enhanced physical models. This will be initially used to run comprehensive simulations under various configurations such as the Catalyst Layer (CL) composition and Gas Diffusion Layer (GDL) porosity distributions and create a detailed dataset of performance metrics such as the current and power densities. The first objective is to enhance the numerical frugality by employing elements of Bayesian optimisation for the data generation. The ultimate purpose is to minimise the number of accurate (and computationally expensive) simulation runs while generating a useful dataset. The next step/objective is to train a Machine Learning (ML)-based surrogate model using simulation data that will mimic the behavior of physical simulations, allowing for quick predictions of key metrics. The ML model along with a stochastic optimisation algorithm will be used to optimise PEMFC parameters (such as the GDL porosity distribution or CL composition) aiming at maximum power density and efficiency. The last objective is to hybridise it with an adjoint solver with properly differentiated ML models. The developed tools will be applied in modern PEMFCs.

The expected results of this research project include: (1) a 3D CFD fuel-cell solver with enhanced efficiency and improved model fidelity due to the use of Hi-Fi physics-based models and ML-tools, (2) reduced computational cost of the 3D CFD fuel-cell model and code and (3) use of the above tools for the effective optimisation and enhanced digital frugality in PEMFC problems. 

Where you will work

For the main part of your work, you will be hosted at the R&D Material Engineering (ME) Division of TME in Zaventem, Belgium. The research group consists of about 15 experienced researchers, who are responsible for the development of new functional materials for hydrogen Fuel Cells (FC) using numerical and experimental methods. The ME Division possesses a HPC platform, including both CPU and GPU nodes, which will be used to support your research program. In addition, you will closely interact with members of the Advanced Technologies (AT) division who are focusing on the development of Artificial Intelligence methods (Machine Learning, Deep Learning) and their application to mobility challenges (i.e., Automated Driving, Carbon Neutral). The cross-divisional nature of the project will also involve the Powertrain division, which is responsible for the control and integration of FC devices in vehicles and stationary applications.

Network activities

Integration within the FairCFD Network

Within the FairCFD network, you will contribute mainly to WP4: Efficient optimization for complex problems. You will regularly exchange with other DCs of the network applying similar approaches to other problems, and/or applying different numerical methods to similar problems.
One secondment (short research stays in other partners of the network) is planned during the PhD at the Parallel CFD & Optimization (PCOpt) Unit of NTUA (Greece) for 6 months for the development of the adjoint method to the fuel-cell model to support gradient-based and hybrid optimisation methods. PCOpt/NTUA is headed by Prof. K.C. Giannakoglou who will be the PhD thesis supervisor. 

Interdisciplinary task: co-designing numerical frugality 

Beyond your individual research program described above, you will contribute along with all other FairCFD doctoral candidates to a network-wide multidisciplinary effort (WP5) addressing the environmental and societal dimensions of numerical simulation.
Each DC will participate in the definition of practical metrics for numerical frugality (computational cost, energy use, resource impact) and contribute data from their simulations to a collective meta-analysis. This initiative will be supported by interdisciplinary experts and accompanied by a dedicated DC in social sciences, who will lead a qualitative study on evolving practices in simulation across the network. Together, we aim to build concrete, informed recommendations for sustainable scientific computing.

Network Training Program — More Than Just a PhD

As a Doctoral Network funded by Marie Sklodowska-Curie Actions (MSCA-DN), FairCFD will offer to you a rich and engaging training experience, including

• Four one-week training events; (i) an induction week devoted to team-building, open-science practices and sustainability issues, (ii) an Essential Skills Accelerator event combining aiming to to equip DCs with essential technical and transferable skills, (iii) a Hackathon event where DCs will collaborate in teams to solve complex physics problem and compare various simulation strategies in terms of precision and sobriety, and (iv) a Career and Leadership Development Forum Aiming to equip DCs with transferable skills essential for their future careers.
• Five On-line courses combining technical training to state-of-the art simulation methods ranging from physics-based approaches to data-driven ones, exposition to industrial applications, along with Social, ethical and environmental aspects of decision-making in modelling practices.
• Involvement in the organisation of scientific events, including a mini-symposium as part of a large-audience scientific conference, a scientific symposium allowing to share the output in terms of new methods, innovation, and applications to industrial processes, and a Societal colloquium to deliver the outputs of the multidisciplinary tasks of the network.

This programme is designed to support your growth as a researcher, innovator, and engaged citizen, fully equipped to lead the next generation of responsible simulation science. See our website for more details (https://www.imft.fr/faircfd/project-presentation/)

Where to apply

The application process will be officially opened on April 1, 2026, till May 31.

The candidates should send a CV, cover letter, BSc and MSc degrees (certified copies plus translation in English) and two letters of recommendation. Copies of publications could be sent later on, upon request. Personal interviews might be asked.

All applications must be mailed here with subject: “FairCFD, application to DC9”.

Requirements

Research Field

Engineering » Mechanical engineering

Engineering » Chemical engineering

Education Level

Master Degree or equivalent

Skills/Qualifications

• Master’s degree (or equivalent) in mechanical or chemical engineering, or related fields.
• Strong background in fluid mechanics, numerical methods, PDEs, and/or data-driven modeling.
• Good programming skills in C++ and experience in the OpenFOAM environment.
• Understanding of the electrochemical processes in a Fuel Cell is welcome.
• Experience in using/developing adjoint-based optimisation methods is welcome.
• Interest in interdisciplinary research and open science.
• Excellent knowledge of written and spoken English (working language).

Additional Informations

Benefits

The successful candidates will receive an attractive salary in accordance with the MSCA regulations for Doctoral Researchers. The exact (net) salary will be confirmed upon appointment and is dependent on local tax regulations and on the country correction factor (to allow for the difference in cost of living in different EU Member States). The salary includes a living allowance, a mobility allowance, and a family allowance (if applicable). The guaranteed PhD funding is for 36 months (i.e., EC funding, additional funding is possible, depending on the local Supervisor, and in accordance with the regular PhD time in the country of origin).

Eligibility criteria

According to the international mobility rules of the MSCA-DN program, the candidates must not have spent more than 12 months in the hosting country (Belgium), during the 36 months preceding the starting of the PhD. Apart from this rule, worldwide applications are expected and encouraged.

Selection process

The application process will be officially opened on April 1, 2026, till May 31. Meanwhile, additional information can be obtained by contacting the supervisors along with the DN coordinating team. For this sake, please contact us by e-mail here, mentioning “FairCFD, application to DC8” in the subject of the e-mail.

Additional comments

A start date will be negotiated with the successful candidate. Ideally start dates would be september 2026.