Curriculum vitæ et studiorum of Edoardo Fabbrini

Professional Experience

• Postdoctoral Researcher

Kyoto University, Graduate School of Science, SACRA (2025/11 – present) Kyoto, Japan

Image-Informed Mechanical Modeling of Lungs for Early Diagnosis and Mechanistic Elucidation of COPD.

• Computational Chemistry (internship)

Daicel Corporation, R&D department, Innovation Park (2023/10) Himeji, Japan

Improved internal software used to optimize ring-opening reactions in lactone derivatives, deploying calculations on Daicel’s computing clusters (Python, ASE, RDKit, Gaussian16).

• Teaching Assistant

Kyushu University (2023/4 - 2023/8) Fukuoka, Japan

Teaching assistant for an introductory first-semester course on the theory of linear partial differential equations, as well as an introductory course on control systems theory.

• Research Assistant

Kyushu University (2022/11 - 2023/8) Fukuoka, Japan

Developed a Python-based computational platform to optimize the properties of functional organic molecules.

• Software Engineer (permanent position)

Alten Italy (2022/9 - 2023/3) Milan, Italy

Developed a plugin for tracking celestial bodies (C++/Qt). Designed and performed software tests using Klaros Test Management.

• Aerospace Software Engineer (permanent position)

TxT Group, assigned to Leonardo Aircraft Division (2021/9 - 2022/3) Milan, Italy

Migrated avionics code for the M-346 aircraft and developed on-board display systems (C++, Ada).

Education

• Ph.D. in Mathematics

Kyushu University, Graduate School of Mathematics (2022/10 - 2025/9) Fukuoka, Japan

Thesis: Analysis and Numerics of Variational Models for Kinematically Incompatible Elasticity.

• Master’s degree in Aeronautical Engineering (GPA: 3.8/4.0, Summa Cum Laude)

Roma Tre University, department of Civil, Computer Science and Aeronautical Engineering (2017/10 - 2021/3) Rome, Italy

Thesis: Stability Analysis of Rotors in Axial Flight Using the Boundary Element Method for Compressible Flows in the Frequency Domain.

• Bachelor’s degree in Mechanical Engineering (GPA: 3.2/4.0)

Roma Tre University, department of Industrial, Electronic and Mechanical Engineering (2012/10 - 2016/12) Rome, Italy

Fellowships & Scholarships

• 2023/4 – 2025/9: JST SPRING MIRAI Scholar. Recipient of the JST SPRING MIRAI Scholarship (Grant Number JPMJSP2136). 2,750,000¥/year (~17,699€/year).
• 2022/11 – 2023/5: Research Assistant at Kyushu University, sponsored by Daicel Corporation.
• FY 2024: Kyushu University Fund Study Abroad Scholarship, 250,000¥ (~1,609€).

Grants

• Short-term Joint Research (2025a033), Kyushu University, FY 2025. 400,000¥ (~ 2,574€).

Research Papers

Research in progress


[5] E. Fabbrini, P. Cesana, A. León Baldelli, M. Morandotti. Numerical analysis of planar linearized elasticity with incompatible kinematics (manuscript in an advanced stage).
[6] T. Hao, J. Y. Fan, J. Diao, A. Le, J. McGee-Odger, P. Bal, E. Fabbrini, J. Xian, D. Vukcevic, J. A. Flegg. Cost Effectiveness Modelling of Present and Future Exotic Mosquito Control Strategies: A Mathematics in Industry Study Group Report for the Department of Agriculture, Fisheries and Forestry (manuscript in an advanced stage).
[7] Y. Kousei, L. Nguyen, A. Staykov, E. Fabbrini, P. Cesana. Prediction of ring-opening activation energy barrier in Diarylethene molecules via two steps Machine learning (manuscript in preparation).
[8] E. Fabbrini, P. Cesana, M. Morandotti. Analysis of regularized kinematically incompatible von-Kármán plates with wedge disclinations and edge dislocations (in progress).

Talks

Research Interests

Applied Mathematics: I use analytical tools from the Calculus of Variations and the theory of elliptic partial differential equations, together with numerical methods such as the Finite Element Method to study elastic materials with topological defects (disclinations and dislocations).
Computational Chemistry: I developed a modular Python-based computational platform to explore the chemical space of diarylethenes (photo-responsive organic molecules) via a custom-designed evolutionary algorithm. The platform implement integrates a machine learning model trained on high-fidelity Density Functional Theory calculations.
Fluidynamics-Structure interactions: My earlier work includes aeroelastic stability analysis of rotating systems in compressible flows using a FORTRAN-based multiphysics tool.