Background:

At GMV, you will join a unique, team-oriented environment where talent, creativity, and commitment are continuously encouraged and challenged. You will become part of an international, multidisciplinary team of more than 100 engineers working on Space Surveillance and Tracking (SST) and Space Traffic Management (STM), contributing directly to space sustainability while boosting your career.

Today, more than one third of all trackable space objects are operational payloads—satellites and spacecraft with manoeuvring capabilities. Building and maintaining high-accuracy catalogues of these objects is essential for preventing collisions and ensuring space sustainability. This requires novel algorithms capable of automatically detecting and estimating manoeuvres in a reliable, efficient, and robust way.

Tasks:

The goal of this thesis is to develop and validate advanced methods for uncertainty quantification in manoeuvre estimation without prior information. You will have the opportunity to:

• Literature review of state-of-the-art techniques in estimation, uncertainty quantification and propagation.
• Surveying existing manoeuvre detection algorithms and identifying their strengths and limitations.
• Performing a comparative analysis of different strategies in realistic orbital scenarios.
• Developing a prototype algorithmic framework, laying the groundwork for future operational use.

Requirements / Skills:

• Enrolment in a Master’s program in Computer Science, Computational Engineering, Mechatronics, Mechanical Engineering, Aerospace Engineering, or a related field.
• Strong programming skills in Python (required); proficiency in C++ is a plus.
• Solid knowledge of astrodynamics (required).
• Background in statistical inference and dynamical systems (beneficial).
• Previous exposure to the lectures “Space Flight Mechanics” and “Space Debris – Risks, Surveillance and Mitigation” is an advantage.

Organisational:

• Start November
• Working student salary