Job offer

Organisation/Company: Ecole Centrale de Nantes

Research Field: Computer science » Digital systems

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

Country: France

Application Deadline: 27 Jun 2025 - 22:00 (UTC)

Type of Contract: Temporary

Job Status: Full-time

Offer Starting Date: 1 Sep 2025

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

We are seeking a highly motivated and talented PhD candidate to join our international research team working on the SAMSARA project. This project aims to enhance the sustainability and reliability of Wire Arc Additive Manufacturing (WAAM) for repair applications by developing advanced data-driven surrogate models. This position is a fully funded 3-year PhD position based at GeM (Institut de Recherche en Génie Civil et Mécanique) at Ecole Centrale de Nantes (ECN), France, with a planned research stay in Singapore at the Institute of High Performance Computing which is part of A*STAR.

Research Focus

The PhD thesis will focus on developing and validating data-driven surrogate models for the WAAM process. The core objective is to create computationally efficient models that can accurately predict and mitigate common defects such as trickling, swelling, and humping, which are caused by heat accumulation during WAAM. The successful candidate will work on integrating high-fidelity simulation data with experimental data to develop robust and reliable models.

This research will contribute directly to the broader goal of making WAAM a more effective tool for the circular economy by optimizing repair processes and reducing waste.

Key Responsibilities

• Conducting a thorough literature review of existing hybrid data-driven modeling techniques.
• Developing novel data-driven surrogate models that integrate simulation and experimental data.
• Incorporating first-principle physics into the models when appropriate, either in the training metrics or the model structure.
• Utilizing and exploring advanced machine learning techniques, such as deep neural networks (DNN), physics-informed neural networks (PINN), and graph neural networks (GNN).
• Processing and integrating experimental data from in-process monitoring to improve the model’s accuracy and applicability.
• Validating the developed models through rigorous testing with both simulated and experimental data.
• Working with team members in France and Singapore to ensure that the models are applicable to a variety of situations.
• Optimizing WAAM process parameters for repair operations using the validated models.
• Disseminating research findings through publications in peer-reviewed journals and presentations at international conferences.

Supervision

The PhD student will be supervised by:

• Assoc. Pr. Lucas Lestandi (GeM-ECN), an expert in data and model reduction, and surrogate modeling in additive manufacturing.
• Pr. Guillaume Racineux (GeM-ECN), the “Directeur de thèse” (thesis director), a specialist in processes and multiphysics modelling such as solid state welding processes and high pulsed power processes.

Strong interaction with the project partners is expected, particularly with:

• Dr. Mark H. John (IHPC), an expert in material modeling and mechanical engineering with strong experience in modeling AM processes.
• Pr. Mathieu Ritou (LS2N), an expert in Smart Manufacturing, monitoring, and Artificial Intelligence applied to manufacturing processes.

Research Environment

The PhD candidate will be part of a dynamic and collaborative research team. The project is a joint effort between leading research institutions in Nantes, France (GeM, LS2N and IMN) and Singapore (IHPC).

The candidate will be hosted in Ecole Centrale de Nantes at GeM lab in the MECNUM team. He/She will have access to state-of-the-art facilities and will have the opportunity to collaborate with international experts in the field. The PhD student is expected to spend time at the IHPC in Singapore to facilitate research collaboration.

Funding

This position is fully funded for 3 years by the SAMSARA project, covering travel expenses and conference attendance.

Minimum Requirements

• A Master’s degree in computer science, data science, applied mathematics, mechanical engineering, or a related field.
• Strong background in at least two of the following: machine learning, data analysis, and numerical modeling.
• Experience with programming languages such as Python, C++ or similar, and machine learning libraries (e.g. TensorFlow, PyTorch).
• Excellent problem-solving and analytical skills.
• Ability to work both independently and as part of an international team.
• Strong communication skills in English (written and oral).
• A basic understanding of additive manufacturing processes, preferably WAAM, is a plus.
• Experience with finite element analysis (FEA) or computational fluid dynamics (CFD).
• Familiarity with experimental data processing and signal analysis.
• Experience with model order reduction techniques or surrogate modeling.