Composing DTs with Variability, Fidelity and Uncertainty

Edtlab; Rennes; On-site; EUR 25,000 - 35,000; Full time

Job summary

Edtlab in Rennes is offering a PhD opportunity focused on digital twins within the Engineering Digital Twin (EDT) program, funded by France 2030. The project aims to advance the foundations of digital twin engineering through innovative methods and collaboration with top research and industry partners.

The candidate will gain skills in system modeling, real-time data processing, and engage with leading organizations such as Inria and CNRS. This position promises recognition in a rapidly growing field with diverse career prospects in research and industry.

Qualifications

• Master or Engineering degree in Software Engineering.
• Experience with modeling (SysML, UML, ...).
• Good level of English.

Responsibilities

• Develop concepts, methods, and tools for digital twin composition.
• Handle variability, fidelity, and uncertainty management.
• Contribute to the Artemis platform.

Skills

Modeling

Real-time data processing

Collaborative innovation

Software Engineering

Good level of English

Education

Master or Engineering degree in Software Engineering

Tools

SysML

UML

Job description

Digital twins are virtual representations of real-world products, systems, or processes, enabling simulation, integration, testing, monitoring, and maintenance. They play a pivotal role in optimizing complex systems across a wide range of domains, from industrial manufacturing and energy to environmental monitoring and healthcare.

The Engineering Digital Twin EDT program, funded by the France 2030 investment plan, is a national initiative aimed at advancing the foundations of digital twin engineering in France and Europe. By bringing together leading academic and industrial partners, EDT seeks to strengthen the bases for the design, use, and deployment of digital twins, addressing key open challenges in model hybridization, composability, development methodologies, digital coupling, and human–twin interaction.

The challenges addressed in this thesis are related to the open question of how DTs could be modularized to allow their composition either at design time or at deployment time, leveraging the notion of contract for their provided and required interfaces, including the management of Variability, Fidelity and Uncertainty. This modularisation also concerns the services provided by the DTs, including data processing and what-if exploration based on eg MachineLearning.

While a model is always “wrong” with respect to reality, some models can be helpful, provided we know about the distance they have with reality. This goes along three dimensions: scale, fidelity and uncertainty management. Scale and Fidelity can be understood as the level of abstraction of the DT (the scale of the map), whereas uncertainty management is the confidence we have in the DT attributes values (for example the river’s width is 10m ± 2m).

Thesis Objectives

This PhD project aims to propose concepts, methods and tools to allow the composition of digital twins components with an explicit handling of Variability, Fidelity and Uncertainty. Key scientific challenges include:

• [Propose a meta-model and an architecture to allow the composition of digital twins]
• [Handle variability modeling as well as automated variability realization, based on UVL and Greal]
• [Handle scale, fidelity and uncertainty management]

The results of this thesis will directly contribute to the Artemis platform, an open-source framework set to become a benchmark in the field.

The PhD candidate will be co-supervised by Jean-Marc Jézéquel and Benoit Combemale, IRISA/DiverSE, as well as Antoine Beugnard, P4S/IMT-Atlantique within the DiverSE team at IRISA, University of Rennes, the best French university in Computer Science (according to ARWU 2025 ranking). The candidate will benefit from a stimulating scientific and industrial environment of the highest level, with access to a national network of leading research institutions and industry partners, regular interactions with the broader EDT community through workshops, seminars, and joint demonstrators, and the opportunity to contribute to Artemis, the program’s open software platform.

What You Will Gain from This PhD

This PhD offers the opportunity to:

• Develop highly sought-after skills in system modeling, real-time data processing, and collaborative innovation.
• Collaborate with leading partners (Inria, CEA, CNRS, etc.) and validate your research on real-world industrial use cases.
• Join a network of PhD candidates within the EDT program, fostering collaboration, peer support, and interdisciplinary exchanges.
• Contribute to an open-source platform (Artemis) and publish in international conferences and journals.
• Gain recognition in a rapidly growing field, with career prospects in academic research, industrial R&D, or entrepreneurship.

Upon completion, you will be positioned as a recognized expert in a key domain for industry and research, with diverse professional opportunities in France and internationally.

References

[1] Benoît Combemale, Pascale Vicat-Blanc, Arnaud Blouin, Hind Bril El Haouzi, Jean-Michel Bruel, Julien Deantoni, Thierry Duval, Sébastien Gérard, & Jean-Marc Jézéquel (2025). Engineering Digital Twins: A Research Roadmap . EDTconf 2025 - 2nd International Conference on Engineering Digital Twins. https://inria.hal.science/hal-05223776

[2] Bernardi, S., Famelis, M., Jézéquel, J., Mirandola, R., Palacin, D. P., Polack, F., & Trubiani, C. (2021). Living with Uncertainty in Model-Based Development. Springer International Publishing. https://doi.org/10.1007/978-3-030-81915-6_8

Foures, D., Acher, M., Barais, O., Combemale, B., Jézéquel, J., & Kienzle, J. (2023). Experience in Specializing a Generic Realization Language for SPL Engineering at Airbus. MODELS 2023 - 26th International Conference on Model-Driven Engineering Languages and Systems. https://doi.org/10.1109/models58315.2023.00035

Requirements

• Master or Engineering degree in Software Engineering
• Experience with modeling (SysML, UML, ...)
• Good level of English

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