Intern in the Robotics and IoT Lab, Stowage Optimisation for Cargo Return Vehicles

Our team and mission

Within the Robotics & IoT Lab, we design and build software capabilities that make human and robotic exploration safer, more efficient, and more autonomous. This internship targets a core challenge in cargo return and logistics: arranging heterogeneous items inside constrained volumes while obeying strict physical and operational rules. Typical constraints include stability, centre-of-mass placement, mass properties, access rules for loading and unloading, orientation limits, keep-out zones, and packaging or interface requirements.

The work you will do feeds directly into mission design and operations. It will help us evaluate stowage options quickly, quantify trade-offs, and provide decision support for engineers and operators. You will collaborate with software engineers, systems engineers, and operations specialists, and you will see your algorithms tested on realistic scenarios and data.

Candidates interested are encouraged to visit the ESA website: http://www.esa.int

Field(s) of activity for the internship

Topic of the internship: Algorithmic Optimisation of Stowage for a Cargo Return Vehicle

You will help develop a numerical optimisation tool for constrained three-dimensional cargo packing in a return vehicle. The goal is to produce high-quality stowage layouts that satisfy physical and operational rules, with clear objective functions such as packing density, stability, mass distribution, access, or load path considerations.

Your work will include:

• Problem modelling: define decision variables, constraints, and objective terms for geometric containment, non-overlap, stability, and mass properties;
• Solver strategy: implement a numerical optimisation tool using either a constraint programming solution with a suitable solver, or a metaheuristic approach, or other as applicable. The choice will be made based on data characteristics and performance in benchmarks;
• Geometric pre-processing: design compact representations that speed up collision and containment checks (for example voxel grids or tree-based spatial indices);
• Physical properties: compute mass properties from item data and verify centre-of-mass limits and inertia targets inside the container;
• Search operators or relaxations: if using a metaheuristic, implement move operators and constructive starts; if using constraint programming, investigate relaxations, cut generation, and search strategies;
• Validation and benchmarking: create test sets, measure solution quality and run times, and document trade-offs between strategies;
• Visualisation for analysis: create simple interactive views to inspect layouts, check constraint violations, and communicate results to non-specialists.

Learning objectives:

• Translate a real operations problem into a formal optimisation model;
• Compare and justify algorithmic choices under changing boundary conditions;
• Build reliable geometric and mass-property checks for three-dimensional layouts;
• Communicate complex results clearly to both technical and non-technical stakeholders.

Behavioural competencies

Result Orientation
Operational Efficiency
Fostering Cooperation
Relationship Management
Continuous Improvement
Forward Thinking

For more information, please refer to ESA Core Behavioural Competencies guidebook

Education

You must be a university student, preferably studying at master’s level. In addition, you must be able to prove that you will be enrolled at your University for the entire duration of the internship.

Additional requirements

The working languages of the Agency are English and French. A good knowledge of one of these is required. Knowledge of another ESA Member State language is an asset.

During the interview, your motivation for applying to this role will be explored.

Essential background and skills:

• Studies in computer science, applied mathematics, robotics, or a related engineering field;
• Good working knowledge of modern C++ and Python – both will be applied in the project;
• Understanding of algorithms, optimisation methods, and data structures;
• Confidence with 3D mathematics (vectors, matrices, transformations) and interest in modelling physical constraints.

Desirable:

• Experience with optimisation or solver libraries;
• Exposure to computational geometry concepts or CAD tools;
• Familiarity with version control (Git) and collaborative coding practices;
• Interest in visualising or debugging 3D layouts.

We are looking for a motivated intern with solid fundamentals and the ability to apply both Python and C++ in practice, while being curious and open to exploring different solution strategies.