Lattice metamaterials
Researching damage-tolerant ultralightweight mechanical metamaterials for future air transportation, with an emphasis on distorted and disordered lattice architectures.
Personal website
Aerospace engineering researcher working at the intersection of metamaterials, simulation, and machine learning.
I am Niccolò Forte, a PhD student in Aerospace Engineering at Queen Mary University of London. My work focuses on lattice metamaterials, computational engineering, and data-driven design methods for future lightweight structures.
- London, UK
- QMUL PhD Researcher
- Mechanical Engineering BEng, First Class
About
A concise academic profile
I have lived and studied across several international cities, including Washington DC, Jerusalem, Rome, Brussels, and London. That background has shaped an open and adaptable approach to research, collaboration, and communication.
Alongside my doctoral work, I teach across engineering modules at Queen Mary University of London and have worked in data science, web development, software testing, and private tutoring. I am especially interested in combining rigorous engineering analysis with practical computational tools.
Research
Current focus
Computational engineering
Using finite element analysis and simulation-led workflows to study structural behaviour, optimization, and mechanical performance.
Machine learning
Exploring data-driven methods, adaptive sampling, and model-based design strategies for multi-objective engineering problems.
Research project
Damage Tolerant Ultralightweight Mechanical Metamaterials for Future Air Transportation
My PhD work develops novel design pathways for distorted lattice metamaterials through stochastic disorder optimization, simulation, and machine learning.
Experience
Selected experience
Teaching Assistant
Queen Mary University of London
Supporting undergraduate and postgraduate engineering teaching across modules spanning fluid mechanics, CFD, solid mechanics, FEA, modelling, simulation, thermodynamics, and engineering management.
Data Science Intern
Red Bull
Worked on commercial data science tasks including market estimates, e-commerce market evaluation, and analytical support for internal decision-making.
IT & Web Developer
Dante Alighieri Project Foundation
Collaborated on website development, digital content, communication, and online visibility for a non-profit foundation.
Private Academic Tutor
Self-employed / MyTutor
Tutored K-12 and university students in mathematics, science, economics, engineering, data science, and computer science, adapting explanations to different levels and learning styles.
Education
Academic background
PhD in Aeronautical Engineering
Queen Mary University of London · 2023 – Present
UKRI EPSRC sponsored research focused on ultralightweight mechanical metamaterials, adaptive sampling, and machine learning for engineering design.
BEng Mechanical Engineering (Hons)
Queen Mary University of London · 2020 – 2023
First Class Honours. Dissertation on predictive modelling of experimental aerofoil aerodynamic coefficients using machine learning.
Skills
Tools and areas
Programming
- Python
- MATLAB
- Bash
- LaTeX
Engineering software
- Abaqus
- ANSYS
- SolidWorks
- Star-CCM+
Technical themes
- Machine learning
- Data science
- CAE
- Mechanical engineering
Languages
- Italian
- English
- French
- Spanish
Contact
Let’s connect
For academic collaborations, research discussions, teaching enquiries, or professional opportunities, feel free to get in touch.