We want to share the story behind our project in a fun and engaging way on LinkedIn. Please answer honestly and, if you like, add a touch of humor or a personal anecdote.
What motivated you to participate in this Marie Curie project?
My motivation to join the ENCODING project came naturally through my long-standing collaboration with the coordinator, Prof. Alessandro Parente. For several years, we have been working together on the topic of data analysis applied to reactive flows, exploring how advanced data-driven techniques can complement physics-based models in combustion research.
When the idea of ENCODING started to take shape, it was clear to me that this project represented a unique opportunity to bring together these complementary approaches — physics and data — in a structured European network. The MSCA DN framework also provided an excellent environment to train young researchers in this hybrid field, fostering collaboration between academia and industry.
From my perspective, participating in ENCODING allows me not only to advance scientific understanding of complex combustion systems, but also to help shape a new generation of scientists capable of leading Europe’s transition toward sustainable energy technologies.
What was the biggest challenge when getting the project started?
One of the main challenges at the beginning of ENCODING was setting up an effective coordination framework among all partners. The consortium brings together institutions with different expertise — combustion, fluid dynamics, data science — and aligning research goals, data-sharing procedures, and training activities required strong communication and organization from the very start.
From a scientific point of view, another major challenge was tackling the complexity of large turbulent combustion datasets. Developing and validating hybrid algorithms that combine physical modeling and data-driven approaches demanded close collaboration between teams, careful validation, and significant computational resources.
Despite these difficulties, these early efforts were essential to build a solid foundation for the project and ensure smooth collaboration later on.
Was it easy to find and select our DCs?
Not at all — it was actually one of the most challenging parts of the project.
ENCODING requires candidates with a rare combination of skills: a solid background in physics or fluid dynamics, together with strong interest or experience in machine learning and data analysis. Finding people who were both technically prepared and motivated to work across disciplines took time and effort.
We received excellent applications, but ensuring the right match between each DC and the specific topic or host institution required a careful and selective process. In the end, though, we are very happy with the outcome — we now have an outstanding group of young researchers who fit perfectly into the spirit of the project.
If you could go back, would you do anything differently in the selection or mentorship of the DCs?Overall, I’m very satisfied with how the recruitment and supervision have gone. If I could go back, perhaps I would start the interaction between DCs and supervisors even earlier, maybe through pre-start workshops or online meetings before the contracts began. That would help them connect as a network and align their expectations from day one.
In terms of mentorship, the experience confirmed how important it is to balance scientific guidance with independence — giving DCs enough freedom to explore their own ideas while keeping them focused on the project’s objectives. It’s rewarding to see how quickly they grow when this balance works well.
What has surprised you the most about how the project has evolved so far?
What has surprised me the most is the strong sense of unity and mutual understanding among all partners. From the very beginning, the consortium has shown excellent coordination and collaboration — both technically and in management aspects.
Even though ENCODING involves very different disciplines and institutions, there has always been a clear shared vision and a genuine willingness to cooperate. The way we communicate, exchange ideas, and support each other has been remarkable, and it has greatly contributed to the smooth progress of the project.
What would you improve in the management or coordination if you could do it again?
The coordination led by ULB has been excellent — very well organized, transparent, and supportive. The project runs extremely smoothly thanks to their clear communication and strong leadership.
If I could suggest one small idea for future projects, I would encourage the Doctoral Candidates themselves to organize short informal technical meetings among those working on related topics. These peer-to-peer exchanges can help them feel even more connected to the network, share ideas more freely, and stay highly motivated throughout their research.
Overall, ENCODING is running exceptionally well — it’s a great example of effective coordination and genuine collaboration across institutions.
Any lessons learned you’d like to share with other PIs or future projects?
One key lesson is the importance of early and transparent communication. Investing time at the start to align expectations, define responsibilities, and set up efficient communication channels really pays off later.
Another lesson is to embrace interdisciplinarity — not just in theory, but in day-to-day supervision and discussions. Projects like ENCODING work best when physicists, engineers, and data scientists truly listen to each other and co-develop solutions.
Finally, having a coordinating team as efficient and committed as the one at ULB makes a huge difference. Good coordination creates trust, and trust is what allows ambitious research to flourish.
What do you hope to see in the next phase of the project?
In the next phase of ENCODING, I hope to see the continuation and strengthening of the excellent collaboration that already exists among all partners. Scientifically, I look forward to seeing the hybrid modeling tools mature and be validated on increasingly complex combustion problems, moving closer to real industrial applications.
I also hope to see the Doctoral Candidates gaining even more confidence and independence — publishing their results, presenting at conferences, and leading collaborations within the network. Their growth as researchers is one of the most rewarding aspects of the project.
Finally, I’d like to see our joint efforts translate into concrete impact: tools and knowledge that support the transition to sustainable combustion technologies and contribute to Europe’s decarbonization goals.
If you had to describe this project with a movie, song, or character, what would it be?
I would probably describe ENCODING as Interstellar. Just like in the movie, we are combining science, technology, and human curiosity to explore something complex and unknown — in our case, the future of sustainable combustion. It’s about pushing boundaries, collaborating across disciplines, and trusting that, together, we can reach something truly innovative.
What is your “secret ritual” to stay motivated during long projects?
For me, it’s all about keeping a sense of curiosity. I try to remind myself that every challenge is part of discovering something new — that’s what keeps research exciting.
Also, I like to celebrate small achievements — a new idea that works, a good discussion with colleagues, or a successful experiment from one of the PhD students. Those moments of progress keep me energized and motivated to move forward.
