Gabriele Immordino, PhD

09/2021 – 10/2025

PhD in Machine Learning

University of Southampton, United Kingdom

Collaboration: Zurich University of Applied Sciences (ZHAW)

Thesis: Data Driven Modelling of Nonlinear Aerodynamics in High Speed Aircraft Using Machine Learning

• Geometric Deep Learning
• Reduced Order Modelling
• Bayesian Neural Networks
• Data Driven Modelling of Physical Systems

10/2017 – 08/2020

Master of Science in Aerospace Engineering

University of Palermo, Italy

Final grade: 110/110

• Computational Fluid Dynamics
• Aerodynamics
• Computational Engineering

09/2014 – 10/2017

Bachelor of Science in Aerospace Engineering

University of Enna, Italy

Final grade: 110/110

09/2025 – 02/2026

Postdoctoral Researcher

ZHAW School of Engineering, IMES Institute for Mechanical Systems — Winterthur, Switzerland

• Development of RAG based LLM pipelines for aviation safety assessments and regulatory compliance
• Design of server side Docker containerised architectures with structured document integration and citation controlled answer generation
• Implementation of Deep Reinforcement Learning for optimisation of control strategies and flight trajectories under multi objective constraints
• Application of Geometric Deep Learning on unstructured 3D meshes for flow field prediction
• Collaboration with academic, industrial, and regulatory partners
• Contribution to teaching activities and presentation of research at international conferences

09/2021 – 09/2025

PhD Researcher

University of Southampton in cooperation with ZHAW

• Development of machine learning models for nonlinear aerodynamics using high fidelity CFD data
• Design of reduced order models using Graph Autoencoders, spatio temporal networks, and Bayesian Neural Networks
• Integration of multi fidelity datasets for improved generalisation and uncertainty quantification
• Construction and validation of large CFD and CAE datasets
• Development of Python based ML pipelines for training, evaluation, and deployment