Job Description:

Leonardo is an international industrial group, among the main global players in Aerospace, Defense and Security, which creates multi-domain technological capabilities in Helicopters, Aircraft, Aerostructures, Electronics, Cyber Security and Space. With over 60,000 employees worldwide, the company has a strong industrial presence in Italy, the United Kingdom, Poland, the United States, and operates in 150 countries also through controlled companies, joint ventures, and participations. A protagonist in the main strategic programs globally, it is a technological and industrial partner of Governments, Defense Administrations, Institutions, and businesses. In 2024, Leonardo recorded consolidated revenues of € 17.8 billion, new orders for € 20.9 billion, and invested € 2.5 billion in R&D activities. Innovation, continuous research, digital industry, and sustainability are the pillars of its business worldwide.

Experienced tutors in their field will follow you, allowing you to delve deeper into the theoretical part and develop your thesis, preparing you in the best possible way for future professional challenges.

The topics proposed for theses to be developed at Leonardo embrace a vast spectrum of technological, research, and innovation areas: from Artificial Intelligence to High-Performance Computing, from Cyber Security to Materials Engineering, passing through aerospace sectors. You will be able to explore the most avant-garde areas of your field of study, with creativity and a spirit of innovation.

To support you during this experience, which can last up to six months, an expense reimbursement is also provided.

We are looking for 1 young student to join us for an internship with the aim of developing their thesis on the topic of “Training virtual entities generated by computer using Reinforcement Learning techniques within a flight simulator” at the Turin site.

The purpose of this thesis is the study, complemented by the development, of flight control software for a synthetic aircraft based on an artificial intelligence model trained with Reinforcement Learning techniques. Flight control is usually dominated by PID (Proportional-Integral-Derivative) logics; although effective, these systems have limitations that Reinforcement Learning (RL) aims to overcome.

A RL model has the ability to learn directly from interaction with the environment and to build an implicit representation of these dynamics, managing to capture complex behaviors. Unlike the fixed parameters of a PID, an RL agent can learn a control policy capable of managing a greater variety of operating conditions. Thanks to training in simulated environments that include variable scenarios, the agent can develop more robust control techniques, improving the stability and performance of the system. While a PID focuses on reducing an error (e.g., maintaining altitude), RL can be trained to maximize a complex reward function that integrates various performance criteria within the same learning process. In this way, the agent can be trained to find the best control policies that simultaneously balance different operational factors, such as precision in following a desired trajectory, fuel saving, and structural integrity.

The main objective of this thesis is the design and implementation of an intelligent agent capable of managing the dynamic control of a synthetic aircraft.

The work will be articulated in the following phases:

• Analysis of commercial products and integration: Study of flight simulators available on the market (e.g., X-Plane 12 or DCS World) as physics engines. The AI agent will interact with the simulator via dedicated APIs or plugins, receiving the aircraft's state (attitude, speed, position) as input and providing control commands for the control surfaces as output;

• Definition of the Learning Model: Design of the “reward function” to train the agent to perform specific maneuvers (e.g., maintaining level flight, intercepting waypoints, or evasive maneuvers) by maximizing the parameters chosen in the project;

• Validation in Synthetic Environment: The trained model will be integrated into a complex scenario, typical of military training standards, to evaluate the AI's ability to react to dynamic variables and environmental unforeseen events.

Educational Qualification: Master's Degree in Computer Engineering or Computer Science.

Seniority: Junior

Technical knowledge and skills:

• Knowledge of programming languages;

• Fundamentals of Machine Learning;

• Deep Learning;

• Reinforcement Learning Techniques.

Behavioral skills:

• Proactivity;

• Ability to work in a team;

• Learning orientation;

• Flexibility;

• Result orientation;

• Interest in the aeronautical world.

Language knowledge:

• Excellent knowledge of written and spoken English, B2/C1 level.

Computer knowledge:

• Programming languages (Python, C++, C#);

• Reinforcement Learning libs;

• Gymnasium;

• GIT/GitHub;

• Data exchange protocols.

How does the selection process work?

Following the collection of applications, the CVs most in line with the required qualifications are evaluated and identified.

Selected candidates will have an exploratory interview with the Human Resources team and with the Business, where technical topics, motivation, and personal aptitudes will be explored.

At the end of the process, the person receives feedback, whether the outcome is positive or negative.

We look forward to your application.

By collaborating with us, you will constantly face the challenges of high technology, enhance your skills, and build a professional path of excellence.

Seniority: Neo-graduate

Primary Location: IT - Turin - C.so Francia

Contract Type:

Total Base Pay Range: Expense reimbursement

Hybrid Working: On-site