Our team and mission

The Technology Department is responsible for the technology strategy, the research and technology development programmes, the education programme and the directorates communication activities.

In particular, this includes, together with all relevant directorates:

• Developing and implementing ESA’s technology strategy;
• Organising studies, research and developments to provide an integrated, continuous technology development path from TRL1 to TRL9 according to strategic and programmatic needs, available competences and resources;
• Coordinating and harmonising technology developments with ESA’s application and programme specific technology development programmes, European and national technology development programmes;
• Developing and implementing a resource and competence plan for conducting R&D activities, together with the Management Support Office and the other departments;
• Preparing future missions and their technologies through early phase studies, system analyses, feasibility assessments, and establishing mission baselines for DG, Directorate, and Member State decisions;
• Developing together with the TEC business partners, Senior Technical Authorities and System engineers effective R&D processes addressing user and programme needs;
• Liaising with D/CIC on commercialisation and competitiveness aspects of R&D activities, and ensure the alignment of ESA's technology strategy with the strategies in these domains;
• Liaising with D/OPS on ground system R&D activities;
• Integrating relevant Education activities into the R&D management processes;
• Communicating the value of ESA’s technical competence, infrastructure and facilities;
• Coordinating in close cooperation with corporate communication at ESA, TEC communication activities;
• Managing TEC internal communication activities.

This internship will take place within ESA's Advanced Concepts Team (ACT). The ACT is a team of scientists who originate from a broad variety of academic fields and are aiming for an academic career. Its task is to monitor, perform and foster research on advanced space systems, innovative concepts and working methods. It interacts externally almost exclusively with academia and operates as a truly interdisciplinary team bound to high scientific standards. Through its research, the team acts as a pathfinder to explore novel, potentially promising areas for ESA and the space sector, ranging from applied to fundamental research topics. An important task of the team is to communicate scientific trends and results, as input to the strategic planning of the Agency. Visit our webpages at Advanced Concepts Team of ESA.

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

Field(s) of activity for the internship

You can choose between the following topics: 

1) Topic 1: Tensor network based methods for constrained combinatorial optimisation problems Systems

Tensor networks (TNs) are computational techniques that originated in many-body quantum physics and have applications in the simulation of complex quantum systems, solving the Navier-Stokes equation, and more. They enable the efficient representation and manipulation of large structured data in high-dimensional spaces. Many combinatorial optimization problems exhibit structural properties (e.g. locality, sparsity, repeated patterns, …) which make them well-suited for tensor network approaches. Recent works have investigated the use of tensor networks to obtain feasible solutions of constrained combinatorial optimization problems [1-4]. More specifically, TNs can be constructed to embed constraints directly into their structure, thus restricting the search only to feasible solutions without relying on penalty functions. Since the search is restricted within the feasible solution space, such methods may prove to be a potentially powerful heuristic.
While theoretical progress has been made regarding the encoding of various constraints, there exists a lack of systematic comparison between tensor network based algorithms and state-of-the-art solvers for various types of combinatorial optimization problems.

Objectives:
The goal of this internship is to obtain a clear picture of the strengths and weaknesses of the latest TN based algorithms as heuristics for solving various relevant constrained combinatorial optimization problems.

The objectives of this internship include:

• A Survey of recent work on tensor network methods applied to (constrained) combinatorial optimisation problems;
• A theoretical study and identification of fundamental limitations and strengths of tensor network based algorithms for various combinatorial optimization problems and constraint types;
• Performing an evaluation and benchmark of promising software implementations of TN-based algorithms, customising and extending code as necessary, and carrying out numerical simulations;
• A comparison of the TN-based approaches with heuristics and exact solvers in terms of solution quality, runtime, and scalability.

References:
[1] H. Nakada, K. Tanahashi, and S. Tanaka, Quick design of feasible tensor networks for constrained combinatorial optimization, Quantum 9, 1799 (2025);
[2] H. Tianyi , H. Xuxin , J. Chunjing , P. Cheng, A Quantum-Inspired Tensor Network Algorithm for Constrained Combinatorial Optimization Problems, Frontiers in Physics,Volume 10 (2022);
[3] J. Lopez-Piqueres, J. Chen, Cons-training tensor networks: Embedding and optimization over discrete linear constraints, SciPost Phys. 18, 192 (2025);
[4] J. Lopez-Piqueres et al., Symmetric tensor networks for generative modeling and constrained combinatorial optimization, Mach. Learn.: Sci. Technol. 4 035009 (2023).

2) Topic 2: On the inversion of the motion field equations

The motion field equations link image-plane motion to 3D scene structure and observer motion under the assumption of a static environment [1]. In planetary or lunar settings—where this assumption holds strongly—these equations offer a powerful tool for inferring egomotion or scene geometry from visual data.
This internship will explore inverting the motion field equations to derive accurate local depth maps/DEMs (Digital Elevation Models) from monocular camera data. Building on an existing framework that combines optical flow and rangefinder data for egomotion estimation on the Moon [3,4], the project will investigate whether the motion field can be inverted directly to estimate a number of quantities, including surface geometry. Additional focus will be given to hybrid approaches using IMU priors, Kalman filtering, and potentially thermal imagery to enhance robustness in low-texture or variable lighting conditions [5,6].

Objectives:
The objectives of this internship include:

• Survey inversion methods for motion field equations in Earth-based contexts;
• Develop algorithms to incorporate multi-beam rangefinder data for complex depth elevation map (DEM) estimation;
• Explore monocular stereographic methods for depth estimation (structure from motion/egomotion aided stereo vision);
• Explore applications in real-time mapping, landing hazard detection, and autonomous navigation.

References:
[1] Horn BK. Motion fields are hardly ever ambiguous. International Journal of Computer Vision, 1988, 1(3): 259–274;
[2] Grabe V, Bulthoff HH, Scaramuzza D, Giordano PR. Nonlinear ego-motion estimation from optical flow for online control of a quadrotor UAV. The International Journal of Robotics Research, 2015, 34(8): 1114–1135.431;
[3] Izzo D, De Croon G. Landing with time-to-contact and ventral optic flow estimates. Journal of Guidance, Control, and Dynamics, 2012, 35(4): 1362–1367;
[4] Vision-Guided Optic Flow Navigation for Small Lunar Missions (Unpublished);
[5] Ho HW, de Croon GC, Chu Q. Distance and velocity estimation using optical flow from a monocular camera. International Journal of Micro Air Vehicles, 2017, 9(3): 198–208;
[6] Zhong S, Chirarattananon P. Direct visual-inertial ego-motion estimation via iterated extended kalman filter. IEEE Robotics and Automation Letters, 2020, 5(2): 1476–1483.

3) Topic 3: Trajectory Design Around Black Holes

Black holes, owing to their extreme density and compact size, exhibit fascinating properties for gravitational flybys. Unlike planets and stars, where the surface prevents very low-periapsis flybys, both Schwarzschild and Kerr metrics allow for greater than 180-degree flybys for both time- and null-like particles [1,2]. The Kerr metric also enables unique ergosphere flyby trajectories and interesting out-of-plane motion. Interstellar mission concepts and a growing interest in venturing beyond the solar system could offer possibilities of sending probes to black holes, providing rare insights into the extreme dynamical environments around these objects. This internship aims to further explore and assess the feasibility of orbital mechanics exploiting the black hole gravitational field.

Objectives:
The objectives of this internship will include some of the following:

• Advance ACT’s current mission analysis framework for gravitational flybys around single black holes (Schwarzschild and Kerr), including tidal gravitational acceleration and time-dilation effects;
• Extend and develop the framework to include mission analysis around binary black hole systems [3];
• Investigate the application of dynamical systems theory to the motion of massive particles in black hole spacetimes (for example: periodic orbits, stable/unstable manifolds);
• Investigate the underlying structure of optimal control theory in curved spacetimes.

References:
[1] Levin, Janna, and Gabe Perez-Giz. “A Periodic Table for Black Hole Orbits.” Physical Review D 77, no. 10 (May 15, 2008): 103005. A periodic table for black hole orbits | Phys. Rev. D;
[2] Grover, Jai, and Alexander Wittig. “Black Hole Shadows and Invariant Phase Space Structures.” Physical Review D 96, no. 2 (July 24, 2017): 024045. Black hole shadows and invariant phase space structures | Phys. Rev. D;
[3] Zhang, Fan. “Gravitational Slingshots around Black Holes in a Binary.” The European Physical Journal Plus 135, no. 1 (January 2020): 104. Gravitational slingshots around black holes in a binary | The European Physical Journal Plus.

4) Topic 4: Liquid-state machines for time-series prediction

Reservoir computing is a machine learning paradigm that leverages the dynamics of a fixed nonlinear system—referred to as a reservoir—to project input signals into a higher-dimensional space, enabling linear methods to solve complex problems. While, in principle, randomly initialised reservoirs should perform equivalently, studies have shown that bio-inspired architectures can offer performance advantages due to their topological and weight distribution properties [1]. Previous work has demonstrated the effectiveness of reservoir computing, particularly Echo State Networks (ESNs), in forecasting trajectories within the Circular Restricted Three-Body Problem (CR3BP) over horizons of up to one month, exhibiting reduced overfitting compared to traditional methods [2].
Building on this foundation, the proposed research will investigate the influence of biologically inspired neuron models on reservoir performance. Specifically, the focus will be on spiking neural networks and biologically plausible models such as Leaky Integrate-and-Fire (LIF) and Izhikevich neurons [3], with the goal of developing and analysing a liquid state machine—an implementation of reservoir computing using spiking dynamics—for trajectory prediction in the CR3BP.

Objectives:
The main objectives of this internship will be the following:

• Adapting the existing echo state network approach to a liquid state machine (using LIF or similar);
• Exploring the effect of biologically accurate spiking neuron models on the performance and properties of the network;
• Extrapolating the findings from the previous steps and implementing the liquid state machine using the most biologically accurate neuron model that is still computationally tractable.

References:
[1] Damicelli, F., Hilgetag, C.C. and Goulas, A., 2022. Brain connectivity meets reservoir computing. PLoS Computational Biology, 18(11), p.e1010639; 
[2] Costi, L., Hadjiivanov, A., Dold, D., Hale, Z.F. and Izzo, D., 2025. The Drosophila connectome as a computational reservoir for time-series prediction. Biomimetics, 10(5), p.341;
[3] Ghosh-Dastidar, S. and Adeli, H., 2009. Spiking neural networks. International journal of neural systems, 19(04), pp.295-308.

5) Topic 5: Simulating Galactic Settlement

How might a space-faring civilisation expand across the galaxy—and under what constraints? A recent study [1] found that even with rapid expansion, steady-state solutions can emerge where only a fraction of the galaxy is settled. This project builds on that premise by exploring how biophysical or astrophysical limits—such as biological scaling laws [2][3], communication costs [4], or stellar kinematics—shape the dynamics of large-scale settlement.
In collaboration with ongoing research at the ACT, the project will implement a tailored simulator or adapt existing tools (e.g., HESTIA [5], GTOCX entries [6]) to investigate whether such constraints permit stable or bounded patterns of expansion. Prior ACT experience from the GTOCX competition can be reused to accelerate development and ensure interoperability.

Objectives:

• Formulate a physically consistent model for galactic settlement dynamics, incorporating key constraints (e.g., travel speed, resource availability, or stellar motion);
• Leverage existing infrastructure from the ACT (e.g., GTOCX-developed code) or external platforms (e.g., HESTIA) to construct or adapt a scalable simulation framework;
• Explore emergent behaviours such as front propagation, fragmentation, or metastable settlement patterns under varying assumptions;
• Assess the existence and stability of steady states, and identify critical parameters that govern the extent and pace of expansion.

References:
[1] Carroll-Nellenback, J., Frank, A., Wright, J. T., & Scharf, C. (2019). The Fermi Paradox and the Aurora Effect: Exo-civilisation Settlement, Expansion, and Steady States. The Astronomical Journal, 158(3), 117. The Fermi Paradox and the Aurora Effect: Exo-civilization Settlement, Expansion, and Steady States - IOPscience;
[2] West, G. B., & Brown, J. H. (2005). The origin of allometric scaling laws in biology from genomes to ecosystems: towards a quantitative unifying theory of biological structure and organization. ''The Journal of experimental biology'', ''208''(Pt 9), 1575–1592. <nowiki>https://doi.org/10.1242/jeb.01589</nowiki>;
[3] Bettencourt, L. M., Lobo, J., Helbing, D., Kühnert, C., & West, G. B. (2007). Growth, innovation, scaling, and the pace of life in cities. ''Proceedings of the National Academy of Sciences of the United States of America'', ''104''(17), 7301–7306;
[4] Bryant, S., & Machta, B. (2023). Physical Constraints in Intracellular Signaling: The Cost of Sending a Bit''. Phys. Rev. Lett., ''131'', 068401.'';
[5] HESTIA – High-resolution Environmental Simulations of The Immediate Area;
[6] Izzo, Dario, Marcus Märtens, Ekin Ozturk, Mate Kisantal, Konstantinos Konstantinidis, Luıs F. Simoes, Chit Hong Yam, and Javier Hernando-Ayuso. "GTOC-X: OUR PLAN TO SETTLE THE GALAXY (ESA-ACT)." (2019).

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.

Additionally, a strong academic background and proven ability to understand and conduct research is an asset.