Job offer

Organisation/Company: Khanh Nguyen

Field: Engineering, Computer Science » Informatics

Researcher Profile: Recognised Researcher (R2), Leading Researcher (R4), First Stage Researcher (R1), Established Researcher (R3)

Country: France

Application Deadline: 30 May 2025 - 22:00 (UTC)

Type of Contract: Temporary

Job Status: Full-time

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

Advancing System-Level Prognostics with Multimodal Data Integration and Uncertainty Quantification

1. Scientific context

Prognostics and Health Management (PHM) plays a critical role in improving availability, reliability, safety, and reducing maintenance costs of complex engineering systems. By predicting the Remaining Useful Life (RUL) of components and systems, PHM enables proactive maintenance, reduces unplanned downtime, and optimizes resource utilization. As modern industrial systems become more complex, with interconnected components and multi-sensor data streams, System-Level Prognostics (SLP) has become indispensable across diverse sectors, including aerospace, energy, manufacturing, and critical infrastructure.

One of the primary challenges in SLP lies in effectively modeling the dependencies and interactions among system components, which significantly influence degradation and failure modes. Conventional methods, such as model-based methods, have addressed these dependencies by leveraging physical prior knowledge of system dynamics. However, as systems grow increasingly complex and sensor data become more high-dimensional, these models often face scalability challenges.

To complement model-based methods, data-driven approaches have gained prominence for their ability to integrate diverse data sources. Bayesian Networks (BNs) provide a robust framework for modeling probabilistic relationships while incorporating historical data, real-time sensor inputs, and expert knowledge. However, BNs also face limitations, including reliance on high-quality historical data and computational complexity.

Hybrid approaches have emerged as a promising solution by leveraging the strengths of both. This thesis aims to address the critical challenges in SLP by developing advanced hybrid approaches that enhance the robustness, scalability, and reliability of prognostics algorithms.

2. Thesis objectives

The proposed methods will introduce transformative strategies for robust data integration, efficient modeling of component interactions, and rigorous uncertainty management. Ultimately, the goal is to establish more accurate and scalable prognostic solutions capable of adapting to increasingly complex engineering systems.

3. Candidate Profile

The ideal candidate should possess the following qualifications:

1. Master's degree or equivalent in a relevant field such as Computer Science, Mechanical/Mechatronic Engineering, Electrical Engineering, or a related discipline.
2. Strong background in machine learning, data analysis, and signal processing.
3. Experience with programming languages such as Python.
4. Familiarity with wind energy systems and/or offshore engineering is desirable.
5. Excellent analytical, problem-solving, and communication skills.
6. Ability to work independently and as part of a team.