The European Space Agency (ESA) is offering an internship in the Technology Department's Advanced Concepts Team (ACT) at ESTEC in Noordwijk, the Netherlands, with four research topics to choose from, from spacecraft trajectory optimisation to orbital control around black holes. Applications close on 30 November 2026 at 23:59 CET/CEST. At Youth Works Hub, we reviewed the full vacancy and set out every topic, objective, requirement and eligibility rule below.
Key Details of the Vacancy
The vacancy lists these formal details:
🆔 Job Requisition ID: 21033
🎓 Type of appointment: Intern
🏛️ Directorate: Technology, Engineering and Quality
📍 Workplace: Noordwijk, NL
🗺️ Location: ESTEC, Noordwijk, Netherlands
📣 Publication method: External Only
📅 Closing date: 30 November 2026, 23:59 CET/CEST
Where You Will Work: ESTEC in Noordwijk
The internship is located at ESTEC in Noordwijk, Netherlands. The role belongs to ESA's Technology, Engineering and Quality Directorate and takes place within the Advanced Concepts Team of the Technology Department.
Who the Advanced Concepts Team Is
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.
How the Advanced Concepts Team Works
The ACT 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.
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Choose Your Internship Topic
The vacancy lets you choose among four topics:
1️⃣ Topic 1: Autodiscovery of search strategies for large Keplerian Travelling Salesperson Problem instances and variants
2️⃣ Topic 2: Informing Guidance and Control Networks through Physics
3️⃣ Topic 3: Low-discrepancy deployment heuristics for competitive mega-constellations
4️⃣ Topic 4: Differentiable simulation and orbital control of shape-changing spacecraft in generic spacetimes
Topic 1: Autodiscovery of Search Strategies for Large Keplerian Travelling Salesperson Problem Instances
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.
Topic 1: Existing Approaches and the Gap
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. However, their performance depends on manually designed elements such as beam-width policies, rollout rules, pheromone updates and local-search procedures.
Topic 1: What the Internship Investigates
Recent work in automated heuristic design combines evolutionary computation and language models to generate and refine optimisation heuristics. This internship investigates 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.
Topic 1: 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 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
Topic 1: References
The vacancy lists these 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.
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.
Topic 2: Why Physics Is Added
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.
Topic 2: 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. The objectives include:
🔍 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
Topic 2: References
The vacancy lists these 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.
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. Conventional Walker constellations provide simple and structured final configurations, but their phasing rules do not directly prescribe how available orbital slots should be filled safely as a populated environment evolves.
Topic 3: What the Internship Develops
This internship develops and evaluates greedy deployment heuristics to determine where a new operator should place its next N satellites within an already populated LEO environment. The approach treats conjunctions as a quantifiable operational cost and seeks insertion rules that preserve a well-spread population in orbital-element space. Low-discrepancy sequences, such as Halton and Sobol' sequences, provide one possible class of space-filling rules; alternative maximin, greedy-packing or risk-aware heuristics are also considered.
Topic 3: Focus and Tools
The focus is on the assignment of orbital phases, primarily mean anomaly and, where relevant, right ascension of the ascending node (RAAN). Satellites are introduced one at a time or in small batches, and each candidate placement is evaluated against the existing population in terms of close approaches, minimum separation and collision-risk indicators. Large orbital populations are propagated and analysed using ESA's cascade library.
Topic 3: Objectives
The objectives include:
🔭 Survey conjunction risk, self-induced collision risk, orbital slotting and deployment strategies for growing LEO mega-constellations
🎯 Develop greedy insertion rules for selecting orbital slots in mean anomaly and RAAN in the presence of an existing multi-operator background population
🔢 Investigate low-discrepancy sequences as incremental space-filling rules, alongside alternative maximin and conjunction-aware placement heuristics
🖥️ Simulate progressive deployment scenarios using ESA's cascade library and evaluate close-approach statistics, minimum-distance distributions and collision-risk indicators at successive growth stages
📊 Compare the proposed heuristics with Walker-type and random-deployment baselines in terms of conjunction frequency, risk, coverage and operational practicality
🧭 Formulate preliminary guidelines for safer incremental deployment in shared, multi-operator LEO environments
Topic 3: References
The vacancy lists these references:
📖 [1] F. Biscani and D. Izzo, "cascade: A C++/Python library for the propagation of large orbital populations and collision detection," European Space Agency, Advanced Concepts Team, 2023. Available at: cascade 0.1.10 documentation
🧾 [2] J. G. Walker, "Satellite Constellations," Journal of the British Interplanetary Society, vol. 37, no. 12, pp. 559–572, 1984.
📄 [3] H. G. Lewis, J. Radtke, J. Beck, B. Bastida Virgili, and H. Krag, "Self-induced collision risk analysis for large constellations," Proceedings of the 7th European Conference on Space Debris, Darmstadt, Germany, 2017. Available at: Self-induced collision risk analysis for large constellations, ESA Proceedings Database
📰 [4] N. Reiland, A. J. Rosengren, R. Malhotra, and C. Bombardelli, "Assessing and Minimizing Collisions in Satellite Mega-Constellations," Advances in Space Research, vol. 67, no. 6, pp. 2013–2025, 2021. doi: 10.1016/j.asr.2021.01.004
🗒️ [5] J. Dick and F. Pillichshammer, Digital Nets and Sequences: Discrepancy Theory and Quasi-Monte Carlo Integration, Cambridge University Press, Cambridge, 2010. doi: 10.1017/CBO9780511761188.
Topic 4: Differentiable Simulation and Orbital Control of Shape-Changing Spacecraft in Generic Spacetimes
A spacecraft with movable internal masses can function as a form of propellantless propulsion when it interacts with a non-uniform gravitational field. By changing its internal geometry at different points along the orbit, the spacecraft modifies its gravitational coupling to the local tidal field. A simple example is a dumbbell spacecraft or a variable-length tether with masses at its ends. Extending and contracting the dumbbell at selected orbital positions can exchange energy and angular momentum with the orbit without expelling propellant.
Topic 4: The Quadrupole Tensor Approach
Harte and collaborators developed a generic description of this mechanism based on the spacecraft's mass quadrupole tensor. Instead of modelling every movable mass or tether element, the spacecraft is represented by a time-dependent symmetric quadrupole tensor. Its eigenvalues describe the principal distribution of mass, while its eigenvectors describe the corresponding principal directions.
Topic 4: Spacetimes and Orbital Effects
The formalism has been developed for a general class of spacetimes, where forces and torques in Kerr and Schwarzschild metrics are known in closed form, though the practical orbital consequences remain unsolved for rotating black holes. Quadrupole modulation can influence orbital energy, eccentricity, apsidal orientation and orbital stability. Jupiter provides an interesting initial test case because of its strong gravity, large tidal gradients, rapid rotation and significant oblateness.
Topic 4: What the Project Investigates
The project investigates whether quadrupole modulation can produce useful secular changes in orbital energy, eccentricity, apsidal orientation, nodal precession or inclination in strong gravitational regimes including Kerr and Schwarzschild metrics.
Topic 4: Objectives
The objectives include:
💻 Developing a differentiable simulator in heyoka for a shape-changing spacecraft implementing Harte's formalism for Schwarzschild and Kerr black holes
✅ Validating the simulator by reproducing published results on eccentricity control, circularisation, apsidal motion and orbital stabilisation
🧮 Applying automatic differentiation and gradient-based optimisation to identify effective deformation amplitudes, frequencies, phases and switching conditions
🪐 Testing on Jupiter's gravity and then reproducing and looking for pure GR effects in a representative Kerr Black Hole
Topic 4: Expected Outcome
The project combines general relativity, analytical mechanics and automatic differentiation. The final outcome is a reproducible computational framework for studying shape-controlled orbital dynamics and evaluating unconventional orbital-control scenarios around exotic gravitational regimes.
Topic 4: References
The vacancy lists these references:
📖 [1] M. Harte and M. Gaffney, "Extended-body effects and rocket-free orbital manoeuvring," Acta Astronautica, vol. 178, pp. 625–633, 2021.
🧾 [2] M. Harte and M. Gaffney, "Extended-body motion in black hole spacetimes: What is possible?", Physical Review D, vol. 102, 124075, 2020.
📄 [3] F. Biscani and D. Izzo, "Revisiting high-order Taylor methods for astrodynamics and celestial mechanics," Monthly Notices of the Royal Astronomical Society, vol. 504, no. 2, pp. 2614–2628, 2021.
Internship Duration and Start Date
According to ESA's announcement of its 2027 student internships, an internship lasts between 3 and 6 months and starts from February 2027. The exact start date is agreed with the ESA tutor.
Who Can Apply: Education
You must be a university student, preferably in your final or second-to-last year of university course at Master's level. You must also be able to prove that you will be enrolled at your university for the entire duration of the internship.
Languages and Additional Requirements
The vacancy sets these requirements:
🗣️ The working languages of the Agency are English and French, and a good knowledge of one of them 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
Behavioural Competencies
The vacancy lists six ESA core behavioural competencies:
🎯 Result Orientation
⚙️ Operational Efficiency
🤝 Fostering Cooperation
💬 Relationship Management
📈 Continuous Improvement
🔮 Forward Thinking
For more information, the vacancy refers candidates to the ESA Core Behavioural Competencies guidebook.
Selection Process and Export Control
During the recruitment process, the Agency may request applicants to undergo selection tests. Applicants must be eligible to access information, technology and hardware which is subject to European or US export control and sanctions regulations.
Eligible Nationalities
Applications are only considered from nationals of one of the following States: Austria, Belgium, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, the Netherlands, Norway, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, and the United Kingdom.
How Many Vacancies You Can Apply For
One candidate can apply for a maximum of 2 vacancies, according to ESA's announcement of its 2027 student internships.
Learn More About ESA
Candidates interested in the role are encouraged to visit the ESA website and the webpages of the Advanced Concepts Team of ESA.
Diversity, Equity and Inclusiveness
ESA is an equal opportunity employer, committed to achieving diversity within the workforce and creating an inclusive working environment. ESA welcomes applications from all qualified candidates irrespective of gender, sexual orientation, ethnicity, religious beliefs, age, disability or other characteristics. People with disabilities are welcome. Whenever possible, ESA seeks to accommodate individuals with disabilities by providing the necessary support at the workplace. The Human Resources Department can also provide assistance during the recruitment process, and you can contact ESA by email using the address in the Links section below.
Allowance and Disclaimer
The vacancy does not state a stipend or allowance. The information published on ESA's careers website regarding internship conditions is correct at the time of publication. It is not intended to be exhaustive and may not address all questions you would have. You can find more opportunities like this in the internships and traineeships section of Youth Works Hub.
Frequently Asked Questions
Where is the ESA Advanced Concepts Team internship based?
The internship is based at ESTEC in Noordwijk, Netherlands, in ESA's Technology, Engineering and Quality Directorate. The vacancy lists Noordwijk, NL as the workplace.
What is the deadline to apply?
The closing date is 30 November 2026 at 23:59 CET/CEST. Applications are submitted through the ESA careers website.
Which topics can I choose from?
You can choose among four topics: Autodiscovery of search strategies for large Keplerian Travelling Salesperson Problem instances and variants; Informing Guidance and Control Networks through Physics; Low-discrepancy deployment heuristics for competitive mega-constellations; and Differentiable simulation and orbital control of shape-changing spacecraft in generic spacetimes.
What is the Advanced Concepts Team?
The ACT is a team of scientists from a broad variety of academic fields who monitor, perform and foster research on advanced space systems, innovative concepts and working methods. It interacts externally almost exclusively with academia and acts as a pathfinder for ESA and the space sector.
What is the Keplerian Traveling Salesperson Problem?
The KTSP is a combinatorial optimisation problem in spacecraft trajectory design, where a spacecraft must visit targets moving on Keplerian orbits. Unlike the classical TSP, transfer costs are time-dependent and asymmetric.
What does the mega-constellation topic work on?
Topic 3 develops greedy deployment heuristics to decide where a new operator should place its next N satellites in an already populated LEO environment. It uses ESA's cascade library to simulate progressive deployment and to evaluate close-approach statistics and collision-risk indicators.
What education level is required?
You must be a university student, preferably in your final or second-to-last year of university course at Master's level. You must also be able to prove that you will be enrolled at your university for the entire duration of the internship.
Which nationalities are eligible?
Applications are only considered from nationals of ESA's listed States: Austria, Belgium, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, the Netherlands, Norway, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, and the United Kingdom.
How long does the internship last and when does it start?
According to ESA's 2027 student internships announcement, an internship lasts between 3 and 6 months and starts from February 2027. The exact start date is agreed with the ESA tutor.
Which languages do I need?
The working languages of the Agency are English and French, and a good knowledge of one of them is required. Knowledge of another ESA Member State language is an asset.
How many ESA internship vacancies can I apply for?
One candidate can apply for a maximum of 2 vacancies, according to ESA's announcement of its 2027 student internships.
What happens during the selection process?
Your motivation for applying to the role will be explored during the interview. ESA may also ask applicants to undergo selection tests, and applicants must be eligible to access information, technology and hardware subject to European or US export control and sanctions regulations.
Does the vacancy state a stipend?
No. The vacancy does not state a stipend or allowance.
Deadline
⏳ 30 November 2026
Links
🔗 Full vacancy and application (Apply now): https://jobs.esa.int/job/Noordwijk-Intern-in-the-Technology-Department-Advanced-Concepts-Team/1452881833/
📘 ESA Student Internships 2027 announcement: https://www.esa.int/About_Us/Careers_at_ESA/ESA_Student_Internships_2027_are_now_open
🌐 ESA website: https://www.esa.int
📧 ESA student internships contact (diversity and accessibility questions): ESA.Student.Internships@esa.int
