Automotive engineering graduate, completing an MSc in Advanced Motorsport Engineering. Currently building a vehicle simulation model at Van Amersfoort Racing.
My experience covers hands-on mechanics, vehicle simulation, data analysis and software development.
Canopy model of the VAR Formula 4 car, built from vehicle parameters and track data.
Two seasons in the suspension department, from design to testing, on two race cars.
C++ overlays for iRacing with a ghost lap database.
Setup optimisation with a neural network, 0.614 s per lap faster.
May 2026 – present
Van Amersfoort Racing (VAR), Zeewolde (NL)
6-month internship in the VAR Talent Program, alongside my Master's thesis.
I am building a vehicle model of the VAR Formula 4 car in Canopy, combining known vehicle parameters with real track data to improve the correlation between simulation and the real car.
Independent project, Geneva (CH)
I helped amateur drivers improve on racing simulators. I analysed their telemetry and gave feedback on driving technique, racing lines and racecraft to improve their consistency and lap times.
Jan 2024 – Nov 2024
Swiss Army, Drognens and Payerne (CH)
Compulsory Swiss military service, done in one continuous period:
2021 – 2023
Bern Racing Team, Biel (CH)
Second season in the suspension department, working on Nebula, the team's new car. My main tasks:
2023 season: 3rd overall at FS Switzerland (10 teams) and 5th overall at FS Czech (37 teams), the team's most successful season at the time.
First season in the suspension department, working on the construction of Adia. My main tasks:
| Event | Overall | Dynamic events | Static events | ||||||
|---|---|---|---|---|---|---|---|---|---|
| End. | AutoX | Acc. | Skid. | Eff. | Des. | BP | Cost | ||
| FS Switzerland | P3/10 | 2 | 3 | – | 3 | – | 6 | 2 | 4 |
| FS ATA | P11/30 | – | 9 | 7 | 5 | – | 10 | 10 | 22 |
| FS Czech | P5/37 | 5 | 6 | 5 | 10 | 3 | 14 | 33 | 29 |
| FS Alpe Adria | P9/36 | 9 | 9 | 12 | 14 | 6 | 25 | 24 | 23 |
| Event | Overall | Dynamic events | Static events | ||||||
|---|---|---|---|---|---|---|---|---|---|
| End. | AutoX | Acc. | Skid. | Eff. | Des. | BP | Cost | ||
| FS Switzerland | P11/15 | – | – | – | – | – | 11 | 6 | 13 |
| FS Alpe Adria | P16/31 | 9 | 16 | 15 | 15 | 9 | 25 | 23 | 16 |
| FS Spain | P28/38 | – | 10 | 7 | – | – | 32 | 27 | 34 |
Dynamic events
Static events
Jul 2022 – Sep 2022
Enrico Fulgenzi Racing, Jesi (IT)
Two-month internship in race car engineering. My tasks:
Personal project
A C++ application for iRacing that displays live data overlays on screen. It uses the iRacing Telemetry SDK and Direct2D for low-latency rendering. Its transparent modules include a digital dashboard, live throttle, brake and steering inputs, blind-spot indicators, a lap time history, a fuel calculator and a G-force meter. They do not affect the simulator's performance.
The main feature is a ghost lap database. The application records the driver's inputs and keeps the three fastest laps for each car and track. While driving, it overlays the current throttle, brake, steering, gear and speed against a chosen ghost lap, on a graph centred on the car's position.
This shows the driver where time is lost and helps refine braking points and consistency, without exporting data to separate analysis software.
2025 – 2026
National Motorsport Academy (NMA)
1st place in a sim racing competition with a fixed car setup, where performance could only come from the driver. I analysed telemetry in MoTeC to find where my braking, throttle and steering inputs lost time, and refined them.
2023
Bern University of Applied Sciences (BFH)
An iOS app (Swift, Xcode) to collect driver feedback, and a MATLAB application to analyse and score car setups tested on a simulator. A neural network trained on this data generated new setups: the best one was 0.614 s per lap faster than the reference setup at Spielberg.
Thesis summary (PDF, in French)
2025 – present
National Motorsport Academy (NMA)
Degree awarded by De Montfort University (DMU), Leicester (UK)
Nine modules: eight taught modules of 15 credits, summarised below, and a 60-credit final project, the master's thesis (in progress).
This module combined a literature review comparing the tyre models used in iRacing and rFactor 2, contrasting empirical, Pacejka-based approaches with more physics-driven modelling, with a technical project exploring whether machine learning could complement traditional tyre modelling. An LSTM-based recurrent neural network was built in Python (TensorFlow/Keras) to learn tyre force, degradation, and wear from simulated telemetry, using track identity as a categorical input alongside continuous time-series data.
This module applied a full systems engineering lifecycle to the design of an autonomous pit-stop traffic light system, intended to reduce team involvement in release timing during a pit stop. The process moved from requirements capture and functional analysis through a structured Failure Modes and Effects Analysis (FMEA), before converging on a final system architecture and component layout.
This module correlated a ChassisSim vehicle model against 7-post rig data to establish a validated baseline, then used the model to explore suspension setup changes aimed at improving mechanical grip and handling balance. Setup quality was evaluated through contact patch load consistency, damper velocity distribution, and handling balance indicators, rather than lap time alone.
This module analysed the organisational structure of a GT3 racing team, covering its competitive programme, roles and responsibilities, operating methods, and regulatory and insurance framework, before developing a full logistics plan for the team's participation in the 24 Heures du Mans. The plan covers transport, accommodation, and legal, health and safety, and personnel compliance requirements.
This module used steady-state CFD to evaluate the aerodynamic and thermal implications of a top-exit exhaust design on a 918 silhouette, across three scenarios: whether exhaust momentum affected downforce and load distribution during cornering, whether low-speed operation created a thermal risk to rear composite structures, and how aerodynamic lift developed with pitch angle at maximum speed.
This module developed a data-based coaching framework combining variance-based time loss localisation, pedal and braking point comparison, and racing line geometry analysis. Derived channels such as slip ratio, neutral steer angle, and grip factor were used to separate technique-driven performance gaps from vehicle or grip limitations.
This module built and validated vertical and lateral dynamic models of the Juno SS3-V6 prototype in MATLAB/Simulink: a 2-DOF quarter car model for vertical response, extended with non-linear bump stop and damping effects, and a 2-DOF single track model benchmarking linear and Pacejka tyre formulations against measured vehicle data to assess handling behaviour at the limit of grip.
This module developed a custom race strategy solver in MATLAB, combining a lap-by-lap simulation with a branch-and-bound Depth-First Search algorithm to generate regulation-compliant pit stop and driver rotation strategies for an endurance race. A dynamic solver adapts the strategy in real time to in-race events such as Full Course Yellow periods or an unplanned incident.
2020 – 2023
Bern University of Applied Sciences (BFH), Biel (CH)
Specialisation: vehicle technology. The programme covers four areas:
Certificate of competence (Engineering orientation) in CAD with NX (advanced), computational fluid dynamics, MATLAB/Octave applications and vehicle electrical engineering.
2019 – 2020
Maturité professionnelle, orientation Technique, architecture et sciences de la vie
Ecole technique – Ecole des métiers Lausanne (ETML), Lausanne (CH)
Technology, Architecture and Life Sciences track, one year full-time after the CFC. It gives access to Swiss universities of applied sciences.
Main subjects: mathematics, physics, chemistry, French, German and English.
2015 – 2019
Mécatronicien d'automobiles CFC, véhicules légers
Ecole technique – Ecole des métiers Lausanne (ETML), Lausanne (CH)
Swiss Federal Diploma of Vocational Education and Training (CFC): a four-year full-time programme combining theory and practice. Main topics:
Practical training in the school workshop, on customer vehicles and during an internship at AMAG, Prangins (CH), Aug 2017 – Jan 2018.