Intern in the Technology Department - Advanced Concepts Team - European Space Agency (Noordwijk)

About this position

The summary below is published by European Space Agency on the official vacancy notice. Our own analysis — salary realism, comparable openings, career trajectory, language profile — follows further down the page.

Key responsibilities

According to the vacancy notice, the role centres on the following duties:

• Develop and evaluate an LLM based framework for automatic discovery of search strategies for large KTSP instances
• Survey KTSP, related spacecraft trajectory optimisation problems, and develop new large instances of KTSP and variants
• Develop a modular representation of heuristic components, including ACO rules, beam-search policies, MCTS rollout strategies, and hybrid approaches
• Implement an agentic framework that generates, evaluates, selects, and refines heuristic variants
• Benchmark on KTSP instances in terms of solution quality, runtime, robustness, and scalability
• Compare automatically discovered strategies with manually designed ACO, beam search, MCTS, and hybrid baselines

Contract and working arrangements

Working arrangements: ESTEC, Noordwijk, Netherlands.

Hiring unit: Advanced Concepts Team, Technology, Engineering and Quality Directorate.

Position details

Reference

1452881833

Last Verified

6 November 2026

Position overview

This is the official EU Careers listing for Intern in the Technology Department - Advanced Concepts Team at ESA based in Noordwijk (Traineeship). The vacancy reference is 1452881833.

Job Requisition ID:21033

Date Posted:1 November 2026

Closing Date:30 November 2026 23:59 CET/CEST

Publication Method:External Only

Directorate:Technology, Engineering and Quality

Location ESTEC, Noordwijk, Netherlands

Our team and mission

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 among the following topics:

1) Topic 1: Autodiscovery of search strategies for large Keplerian Travelling Salesperson Problem instances and variants

The Keplerian Traveling Salesperson Problem (KTSP) is a combinatorial optimisation problem arising in spacecraft trajectory design, where a spacecraft must visit targets moving on Keplerian orbits. In contrast to the classical TSP, transfer costs are time-dependent and asymmetric. The KTSP is relevant to applications including active debris removal, in-orbit servicing, and multi-rendezvous mission planning [1–3].

Existing approaches use metaheuristics such as Ant Colony Optimisation (ACO), beam search, and Monte Carlo Tree Search (MCTS). Hybrid methods, for example Beam P-ACO, combine probabilistic solution construction with bounded tree search and have shown promising results for multi-rendezvous trajectory optimisation [3]. However, their performance depends on manually designed elements such as beam-width policies, rollout rules, pheromone updates, and local-search procedures.

Recent work in automated heuristic design combines evolutionary computation and language models to generate and refine optimisation heuristics. This internship will investigate whether an open framework based on evolutionary search and compact open-source language models (e.g. CHATOSS) can automatically discover effective search strategies for large KTSP instances and variants [4–6].

Objectives: The goal of this internship is to develop and evaluate an LLM based framework for the automatic discovery of search strategies for large KTSP instances.

The objectives of this internship include:

- a survey of KTSP, related spacecraft trajectory optimisation problems, and the development of new large instances of KTSP and a few useful variants; - development of a modular representation of heuristic components, including ACO rules, beam-search policies, MCTS rollout strategies, and hybrid approaches; - implementation of an agentic framework that generates, evaluates, selects, and refines heuristic variants; - benchmarking on KTSP instances in terms of solution quality, runtime, robustness, and scalability; - comparison of automatically discovered strategies with manually designed ACO, beam search, MCTS, and hybrid baselines.

References: [1] M. Bannach, G. Acciarini, and D. Izzo, The Keplerian Traveling Salesperson Problem, arXiv:2605.00010, 2026. [2] D. Izzo, I. Getzner, D. Hennes, and L. F. Simões, Evolving solutions to TSP variants for active space debris removal, Proceedings of GECCO, 2015. [3] L. F. Simões, D. Izzo, E. Haasdijk, and A. E. Eiben, Multi-rendezvous spacecraft trajectory optimization with beam P-ACO, Evolutionary Computation in Combinatorial Optimization, 2017. [4] M. Schmidt and H. Lipson, Distilling free-form natural laws from experimental data, Science, 324(5923), 81–85, 2009. [5] F. Liu, X. Tong, M. Yuan, X. Lin, F. Luo, Z. Wang, Z. Lu, and Q. Zhang, Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model, Proceedings of the 41st International Conference on Machine Learning, PMLR 235, 32201–32223, 2024. [6] H. Ye, J. Wang, Z. Cao, F. Berto, C. Hua, H. Kim, J. Park, and G. Song, ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution, Advances in Neural Information Processing Systems 37, 43571–43608, 2024.

2) Topic 2: Informing Guidance and Control Networks through Physics

Guidance and Control Networks (G&CNETs) are neural architectures that map the current state of a system directly to control actions, combining guidance and control in a single inference step. They are commonly trained using behavioural cloning: large datasets of optimal trajectories are generated offline and used to learn state-action mappings. This approach has been successfully demonstrated for spacecraft landing as well as low-thrust interplanetary transfers, where neural policies can reproduce near-optimal guidance in real time [1–3].

However, supervised learning alone does not explicitly enforce the optimality principles that generated the training data, such as Pontryagin’s Maximum Principle, the Hamilton–Jacobi–Bellman (HJB) equation, Hamiltonian minimisation, and costate dynamics. Recent work has highlighted the potential of embedding these principles directly into neural guidance architectures, while physics-informed and Pontryagin neural networks have been applied to low-thrust transfers and planetary landing problems [4–5].

Objectives: The goal of this internship is to develop the next generation of G&CNETs architecture combining behavioural cloning with optimal-control constraints during training in a PINN-like architecture.

- a survey of G&CNETs, physics-informed neural networks, and neural methods based on the HJB equation and Pontryagin’s Maximum Principle; - development and test of composite training losses combining supervised control imitation with HJB residuals, Hamiltonian minimisation, and costate-consistency constraints; - validation on benchmark spacecraft guidance problems, such as planetary landing or low-thrust interplanetary transfers; - comparison with standard supervised G&CNETs in terms of accuracy, robustness, closed-loop performance, and propellant optimality.

References: [1] C. Sánchez-Sánchez and D. Izzo, Real-Time Optimal Control via Deep Neural Networks: Study on Landing Problems, Journal of Guidance, Control, and Dynamics, 41(5), 2018. [2] D. Izzo and E. Öztürk, Real-Time Optimal Guidance and Control for Interplanetary Transfers Using Deep Networks, Journal of Guidance, Control, and Dynamics, 44(2), 315–327, 2021. [3] D. Izzo, E. Blazquez, R. Ferede, S. Origer, C. De Wagter, and G. C. H. E. de Croon, Optimality Principles in Spacecraft Neural Guidance and Control, Science Robotics, 9(91), 2024. [4] D. Izzo, E. Öztürk, and M. Märtens, Interplanetary Transfers via Deep Representations of the Optimal Policy and/or of the Value Function, 2019. [5] A. Addario et al., Learning Fuel-Optimal Trajectories for Space Applications via Pontryagin Neural Networks, Aerospace, 11(3), 228, 2024.

3) Topic 3: Low-discrepancy deployment heuristics for competitive mega-constellations

Mega-constellations in low Earth orbit (LEO) are progressively deployed and replenished as multiple operators launch, insert, and replace satellites over several years. During this growth phase, the geometry of individual constellations and of the shared orbital environment changes continuously, generating close approaches both within and between satellite systems. Studies of large constellations have shown that self-induced conjunctions can represent an important component of the overall collision risk [3,4]. Conventional Walker constellations provide simple and structured final configurations [2], but their phasing rules do not directly prescribe how available orbital slots should be filled safely as a populated environment evolves.

This internship will develop and evaluate greedy deployment heuristics to determine where a new operator should place its next N satellites within an already populated LEO environment. The approach will treat conjunctions as a quantifiable operational cost and seek insertion rules that preserve a well-spread population in orbital-element space. Low-discrepancy sequences, such as Halton and Sobol' sequences, will provide one possible class of space-filling rules [5]; alternative maximin, greedy-packing, or risk-aware heuristics will also be considered.

The focus will be on the assignment of orbital phases, primarily mean anomaly and, where relevant, right ascension of the ascending node (RAAN). Satellites will be introduced one at a time or in small batches, and each candidate placement will be evaluated against the existing population in terms of close approaches, minimum separation, and collision-risk indicators. Large orbital populations will be propagated and analysed using ESA's cascade library [1].

Application timeline

This vacancy was first listed on 4 November 2026, 3 days ago.

The application deadline is 30 November 2026, 21 days from today. Late applications are not accepted, so submit through the official EU Careers portal well in advance.

Last verified against the EU Careers feed on 6 November 2026.

Where to learn more

For headcount, mission, and other open vacancies at ESA see the ESA institution page; for the cost of living, correction coefficient, and other postings in Noordwijk see our Noordwijk location page.

New to EU careers? Our beginner's guide walks through entry routes, EPSO competitions, and what to prepare. For application logistics see application tips and EPSO competitions.