ESA Graduate Trainee in Scientific Deep Learning

This graduate traineeship is for an ESA Graduate Trainee in Scientific Deep Learning within ESA's Advanced Concepts Team (ACT) in the Technology Department.

Our team and mission

ESA’s Advanced Concepts Team (ACT) monitors, performs, and promotes cutting-edge multidisciplinary research for space. It explores innovative approaches to space-related R&D, including competitions, prizes, and games, as well as research aimed at fostering disruptive innovation. The team develops an expert network within academia and provides rapid first-look analyses of challenges, opportunities, and problems.

The ACT collaborates with universities and research centers, focusing on advanced topics of strategic relevance to the space sector while experimenting with novel teamwork methods. To achieve its objectives, the ACT fosters a dynamic, multidisciplinary research environment where early-career researchers—spanning postdoctoral and postgraduate levels in science and engineering—contribute to the development of emerging technologies and innovative concepts.

Field(s) of activity/research for the traineeship

You will carry out most of your activities in the field of scientific deep learning, with a strong focus on geometric deep learning and other advanced machine learning techniques designed to learn from structured, relational, and physically grounded data. Scientific deep learning aims to integrate data-driven methods with domain knowledge, physical constraints, and mathematical structure in order to build models that are robust, interpretable, and learn in data-scarce settings. Geometric deep learning extends classical neural networks to non-Euclidean domains such as graphs, meshes, manifolds, and spheres. These methods are particularly well suited to problems where the underlying data exhibit symmetries, conservation laws, or relational structure.

You will explore how such techniques can be used to model complex systems, learn representations of physical processes, and enable more reliable inference and prediction in scientific and engineering contexts such as gravity inversion, optimal control, and advanced materials. You are encouraged to propose your own research ideas within this broad scope.

Possible activities include, but are not limited to:

• learning on sphere, graphs, meshes, and manifolds for modelling physical systems and relational data;
• equivariant and invariant neural architectures that respect geometric symmetries;
• hybrid approaches combining deep learning with numerical simulation and optimisation;
• representation learning for high-dimensional, sparse, or multi-modal scientific data;
• physics-informed and constraint-aware neural networks that incorporate prior knowledge and governing equations;
• uncertainty-aware and probabilistic deep learning methods for scientific applications;
• development of open-source research software and reproducible machine learning pipelines.

Throughout the traineeship, you will strengthen your understanding of modern machine learning theory while gaining hands-on experience with applications on dynamical systems. You will learn how to translate scientific questions into machine learning problems, design appropriate architectures exploiting symmetries in the data, evaluate models critically, and communicate results clearly to both technical and non-technical audiences.

As a member of the Advanced Concepts Team (ACT), you will contribute to the development and evaluation of new space technologies and concepts. You will collaborate with experts from diverse fields, including artificial intelligence, computer science, fundamental physics, and mission analysis. Depending on your background and interests, your work may include various initiatives, such as competitions organized via the ESA’s ACT optimize platform and/or studies conducted under ESA’s Ariadna scheme, and you will help disseminate research findings within ESA and to external audiences.

Finally, you will monitor—and if feasible, contribute to—ESA's Discovery and Preparation campaigns by refining early study definitions and possibly participating in select activities.

Technical competencies

• Knowledge of relevant technical/functional domains
• Relevant experience gained during internships, project work and/or extracurricular or other activities
• General knowledge of the space sector and relevant activities
• Knowledge of ESA and its programmes/projects

Behavioural competencies

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

Education

You should have just completed, or be in the final year of your master’s degree in .

Additional requirements

You should have good interpersonal and communication skills and should be able to work in a multicultural environment, both independently and as part of a team. Previous experience of working in international teams can be considered an asset.

• Basic knowledge of machine learning pipelines and best practices.
• Hands-on experience with Python and/or C++, and state-of-the-art machine learning tools such as PyTorch, TensorFlow, or JAX.
• A solid understanding of core mathematical concepts such as linear algebra, probability theory, optimization, and statistical learning theory.
• Strong inclination toward the theoretical foundations of machine learning and deep learning.

Important Information and Disclaimer

Applicants must be eligible to access information, technology, and hardware which is subject to European or US export control and sanctions regulations & eligible to acquire the security clearance by their national security administrations.

Nationality and Languages

Please note that applications can only be considered from nationals of one of the following States: Austria, Belgium, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Luxembourg, the Netherlands, Norway, Poland, Portugal, Romania, Slovenia, Spain, Sweden, Switzerland, and the United Kingdom. Nationals from Latvia, Lithuania and Slovakia as Associate Member States, or Canada as a Cooperating State, can apply as well as those from Bulgaria, Croatia, Cyprus and Malta as European Cooperating States (ECS).

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

*Member States, Associate Members or Cooperating States.