How to ensure sufficient data richness for the estimation of stochastic dynamical systems in fi[...]

Centre de Recherche en Automatique de Nancy ( CRAN ); France; Sur place; EUR 40 000 - 60 000; Plein temps

Résumé du poste

A research center in France is seeking a candidate for a temporary position in system identification and control engineering. The role involves developing conditions for data informativity based on stochastic noise. Ideal applicants will hold a master's degree or be nearing completion, with skills in machine learning, data analysis, and control engineering. Proficiency in English is necessary, while French is not mandatory. The position is located in Nancy and is set to begin in October 2026.

Qualifications

• Graduated or in the final year of a Master’s program or engineering school degree.
• Skills in control engineering, system identification, data analysis, machine learning, or applied mathematics.
• Good level of English (min B2) is required; proficiency in French is not mandatory.

Responsabilités

• Develop necessary and sufficient conditions on excitation for data informativity.
• Extend analysis to general scenarios, including closed-loop identification.
• Conduct research in the framework of linear system identification.

Connaissances

Control engineering

System identification

Data analysis

Machine learning

Applied mathematics

Good level of English (min B2)

Formation

Master’s program or engineering school degree

Description du poste

Organisation/Company Centre de Recherche en Automatique de Nancy ( CRAN )

Research Field Computer science » Database management Engineering Researcher Profile Recognised Researcher (R2) Leading Researcher (R4) First Stage Researcher (R1) Established Researcher (R3)

Country France

Application Deadline 19 Feb 2026 - 22:00 (UTC)

Type of Contract Temporary Job Status Full-time Offer Starting Date 1 Oct 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

For most real-world dynamical systems, input–output models are developed for control, optimization, prediction, or diagnosis. However, the system dynamics are often unknown. Data-driven modeling, combining system identification and machine learning techniques, provides an effective strategy to determine a model from input–output data collected during excitation experiments. The user selects a structure that groups several candidate models. These models are ranked according to their ability to explain the data. The identified model is the one with the optimal score.

The property that guarantees the uniqueness of the optimal model is called data informativity. It indicates whether the data contain sufficient information about the system dynamics. Introduced in the 1980s, this concept led to the establishment of necessary and sufficient conditions on excitation for the identification of linear time-invariant (LTI) systems. However, these studies focused on the asymptotic case, assuming an infinite amount of data, which is unrealistic. More recently, informativity has been studied in the context of a finite number of data points, either in the noise-free case or with deterministic and bounded noise. In practice, these assumptions are rarely satisfied since noise is often stochastic.

The central question of this thesis is the development of necessary and sufficient conditions on excitation to guarantee the informativity of a finite number of data affected by stochastic noise, in the framework of linear system identification. The approach proposed in a recent work at CRAN will be used as a starting point. However, the simplifying assumptions in that work limit its applicability. The objective is therefore to extend the analysis to more general scenarios, particularly closed-loop identification and linear parameter-varying systems, which are better suited for complex systems.

References

• [1] Bazanella et al., “Necessary and sufficient conditions for uniqueness of the minimum in prediction error identification,” Automatica, 48(8):1621–1630, 2012.
• [2] Colin et al., “Closed-loop identification of MIMO systems in the prediction error framework: Data informativity analysis,” Automatica, 121:109171, 2020.
• [3] Colin et al., “Data informativity for the open-loop identification of MIMO systems in the prediction error framework,” Automatica, 117:109000, 2020.
• [4] Gevers et al., “Informative data: How to get just sufficiently rich?,” Proceedings of the 47th IEEE Conference on Decision and Control, pp.1962–1967, 2008.
• [5] Ljung, System Identification: Theory for the User, Prentice Hall, 1999.
• [6] Sleiman et al., “Data informativity for prediction error identification of stochastic LTI systems with repeated finite-time experiments in open-loop,” 2025.
• [7] van Waarde et al., “A behavioral approach to data-driven control with noisy input–output data,” IEEE Transactions on Automatic Control, 69(2):813–827, 2023.
• [8] van Waarde et al., “Data informativity: A new perspective on data-driven analysis and control,” IEEE Transactions on Automatic Control, 65(11):4753–4768, 2020.
• [9] Willems et al., “A note on persistency of excitation,” Systems & Control Letters, 54(4):325–329, 2005.

We are looking for a candidate who has graduated or is in the final year of a Master’s program or an engineering school degree with skills in control engineering, system identification, data analysis, machine learning or applied mathematics. A good level of English (min B2) is required and proficiency in French is not mandatory.

Additional Information

Work Location(s)

Number of offers available 1

Company/Institute Centre de Recherche en Automatique de Nancy ( CRAN )

Country France

City Nancy Geofield