You can view my resume in PDF format, as well as a more detailed overview of my professional experiences, below. Please feel free to contact me for more details or to ask for my references.



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INRIA

03/25 - 03/26 – Rennes

Research engineer

    I worked on a numerical simulation project focused on composite manufacturing processes, with the goal of modeling the flow of liquid resin within a porous reinforcement material for any geometry and under any resin injection conditions. As a research engineer, my work focused on three main areas:

PINN approach (Physics-Informed Neural Network):

  • GUI: integrate the model into a graphical user interface to facilitate data entry and generate high-quality 3D renderings.
  • Extended features: add a new loss function based on sensor data and revise the model to include multiple input parameters.
  • Optimizer: Compare different optimization algorithms and hyperparameter values.
  • Optimization & convergence: mixed precision, JIT compilation, Fourier embeddings, loss balanced, sampling strategy.

FEM solver approach

  • Geometries & meshes: build a library of 2D/3D geometric models and generate optimized meshes.
  • FEM simulations: modify an open-source FEM solver and provide high-fidelity reference data.
  • Validation: cross-validate the PINN model predictions with FEM solver results to ensure reliability and accuracy.

GNN approach (Graph Neural Networks)

  • Implementation: develop a Graph Neural Network (GNN) with an Encoder-Processor-Decoder architecture to process FEM simulation data.
  • Training: train the GNN model to learn spatial and physical relationships within the mesh-based data.
  • Benchmarking & validation: fine-tune the hyperparameters and validate the optimized GNN model on unseen geometric simulations.

Additional responsibilities

  • Documentation & repository management: maintain the team’s GitLab repository and ensure clear documentation.
  • Deployment and scalability: ensure that code and models can be deployed across different hardware configurations.

Outcomes & collaborations

    The project sparked collaborative discussions with companies in the energy and aerospace sectors. Our team was invited to present at major industry events, such as AIComp and JEC World.

🛠️    Python, MATLAB, TensorFlow, Pytorch, GPU computation, Gmsh, Shell scripting, Dockerfile, Makefile



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AIRBUS Operations SAS

03/24 - 09/24 – Toulouse

Intern in numerical simulations in design office

    I built a surrogate model to predict stress outputs generated during wing-fuselage assembly phase of an A321Neo aircraft. Based on machine learning methods, the model was able to solve stresses continuous-space regression for any gap/clash scenario between wing and fuselage. My main activities were to:

  • Benchmark: test multiple machine learning models and select the one best suited to the problem.
  • Sampling strategy: use an unsupervised machine learning approach based on the history of gap/clash values observed in production to identify relevant scenarios for simulation.
  • Data generation: automate Abaqus simulation runs of the rear spar’s detailled finite element model with Python and Shell scripts and generate efficiently training and testing data from scenario sampling.
  • Training and validation: train the surrogate model and build a full validation process to assess the performance of the model.

    The predicted stress values in the rear spar of an A321Neo for an unseen scenario were closer to the stress results obtained from finite-element method simulations. This surrogate model is now being tested on other Airbus aircraft models or other structural components.

🛠️    Abaqus Script, Python, Scikit-Learn, Shell scripts, Finite-Elements Analysis



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University of Naples Federico II

02/23 - 08/23 – Naples

Intern in aircraft design

   Attached to the Design Aircraft and Flight (DAF) research division, I worked on the hybridization parameters exploration with Monte Carlo method for hybrid-electric distributed propulsion aircraft. The aim was to provide a description of the effects of these parameters on the performance constraints, the energy consumption, and more generally on the aircraft design sizing process. The large number of possible combinations means that sizing algorithms need to be coupled with statistical computation method such as Monte-Carlo method. My objectives were to:

  • OpenVSP simulation: demonstrate the improved aerodynamic performance of a distributed propulsion aircraft during takeoff and climb.
  • MATLAB coding: code a sizing algorithm based on the works of Delft University of Technology for an hydrid ATR 72-600 aircraft.
  • Monte-Carlo computation: generate thousands of aircraft designs with random parameter values.

    I concluded that the set of convergent designs is located in a delimited space. By choosing precise hybridization parameter values for each flight phase, I designed a draft version of an optimal hybrid aircraft with the lowest takeoff weight, saving between 6.5% and 16% in kerosene in a short term configuration of battery technology scenario. My draft design and results were compared with DAF’s work, which helped reinforce confidence in their high-fidelity methods and aircraft sizing models. During this internship, I communicated with my team in Italian and wrote in English.

🛠️    MATLAB, Git, LaTex, OpenVSP, academic paper



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ELISA Aerospace

09/19 - 09/24 – Saint-Quentin

School of aerospace engineering

  • Degree: with honors mention given by the exam board.
  • Courses completed: Computational science, Statistics and Optimization, Flight Mechanics and Aerodynamics, Finite Element Method, EEA (Electrical Engineering, Energy, and Automation)

🛠️    C++, Qt, Abaqus Standard/CAE, Ansys Fluent, 3D experience, Arduino