Research Engineer

Stefano Cassola

Bridging mechanical engineering with AI — from motorsport tires to flow field prediction with neural networks.

About Me

Mechanical engineer with a unique trajectory spanning 4 years of hands-on motorsport tire development at Goodyear-Dunlop and 6+ years of research at Leibniz-Institut für Verbundwerkstoffe (IVW). Currently completing a PhD at RPTU Kaiserslautern-Landau focused on using machine learning — specifically CNNs and Fourier Neural Operators — to predict 3D flow velocity fields in fibrous microstructures. Combines deep domain expertise in composite materials processing with modern ML/AI methods, high-performance computing, and CFD simulation. DAAD scholarship recipient with international research experience at the University of Delaware.

Kaiserslautern area, Germany
German (native), English (C2), Italian (A1)

Professional Experience

April 2020 — Present

Research Assistant — Process Simulation / PhD Candidate

Leibniz-Institut für Verbundwerkstoffe (IVW)

Kaiserslautern, Germany

PythonTensorFlowOpenFOAMLS-DYNACNNFNOHPC

Dec 2023 — Jan 2024

Visiting Researcher

Center for Composite Materials (CCM), University of Delaware

Newark, DE, USA

LIMSCompositesInternational

June 2016 — March 2020

Development Engineer — Motorsport Tires

Goodyear-Dunlop Tires Germany GmbH

Hanau, Germany

Data AnalysisMotorsportProduct DevelopmentTesting

Sep 2015 — Feb 2016

Work and Travel

Australia & Southeast Asia

Australia / Southeast Asia

International Experience

Oct 2014 — Jun 2015

Student Research Assistant

Leibniz-Institut für Verbundwerkstoffe (IVW)

Kaiserslautern, Germany

CompositesPermeabilityTesting

Jan 2014 — Jul 2014

Intern — Chassis Development

BMW AG Motorrad

Munich, Germany

CATIA V5CADChassisPrototyping

Mar 2010 — Nov 2013

Mechanic (Working Student)

Waldemar Löser GmbH & Co. KG

Speyer, Germany

ManufacturingMachiningHands-on

Publications

Journal Article2022First Author

Machine Learning for Polymer Composites Process Simulation — a Review

S. Cassola, T. Schmidt, M. Duhovic, D. May

Composites Part B

View Paper
Journal Article2024Co-author

Microscale domain permeability prediction of fiber reinforcement structures based on the lattice Boltzmann method and machine learning

M. Novitska, S. Cassola, T. Schmidt, M. Duhovic, B. I. Basok, D. May

Journal of Porous Media

View Paper
Conference2023Speaker

Physics informed neural networks for permeability predictions of fibrous microstructures

D. Korolev, S. Cassola (Speaker), M. Hintermüller, T. Schmidt, M. Duhovic, D. May

Artificial Intelligence in Material Science and Engineering, Saarbrücken

Conference2023First Author

Simulating the hot press processing of structural thermoplastic foams

S. Cassola, M. Duhovic, M. Salmins, P. Mitschang

14th European LS-DYNA Users Conference, Baden-Baden

Conference2022Co-author

Towards Faster 3D Simulations of CFRTP Induction Welding

M. Duhovic, S. Cassola, T. Hoffmann, P. Mitschang

16th LS-DYNA Forum, Bamberg

Conference2021First Author

Forming and spring-back simulation of CF-PEEK tape preforms

S. Cassola, M. Duhovic, D. Schommer, J. Weber, J. Schlimbach, J. Hausmann

13th European LS-DYNA Users Conference, Ulm

Education & Certificates

PhD in Mechanical Engineering

RPTU Kaiserslautern-Landau

April 2020 — Present (expected Sep 2026)

Research at IVW. Topic: Flow field prediction in fibrous microstructures using machine learning.

Diploma in Mechanical and Process Engineering (Dipl.-Ing.)

University of Kaiserslautern (now RPTU)

October 2008 — September 2015

Specialization: Vehicle and Energy Technology. Grade: 1.6 (very good). Equivalent to combined BSc + MSc. Thesis (1.0): "Analysis of Textile Behavior of Carbon Fiber Fabrics during Continuous Preforming".

Continuing Education

TorchPhysics: Deep Learning for PDEs (Nov 2025)
Software Version Control with Git (Sep 2024)
Python for Advanced Users — TU Nachwuchsring (Jul 2022)
Leibniz Summer School: Mathematical Methods for ML (Aug 2021)
Statistics — Basics to Scientific Application (Jun 2021)
DeepLearning.AI TensorFlow Developer Certificate (Apr–May 2021)
Deep Learning Specialization — Coursera (Feb–Apr 2021)
C++ for Advanced Users — TU RHRK (Apr 2021)

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