Below are the main projects being offered in 2021.


ESAC supervisor(s): Jakob Livschitz, Matthias Ehle

Adoption of modern technologies within ATHENA collaborative development environment: automation of requirements synchronization between DOORS and JIRA/Confluence, workflows automation, integration of parameter database, etc.

Duration: 6 months.

Desirable expertise or programming language:

  • Knowledge and experience with Python, HTTP and REST API is mandatory.
  • Knowledge of Atlassian tool suite,  IBM Rational DOORS and Agile approach is beneficial.

To apply for this project please go here.


ESAC supervisor(s): Santa Martinez Sanmartín, Sebastien Besse, Csilla Orgel, Elliot Sefton-Nash, David Heather

The fleet of new missions and instruments sent to the Moon since 2007 offer new capabilities in the examination of the lunar surface. Large volcanic lava flows, also known as "Mare Basalts" consist of individual flows that can be identified in several different ways. Improvements in data from the latest instruments have shown that the previous mapping of lava flow boundaries can be updated. New mineralogical boundaries have been identified using the Moon Mineralogy Mapper observations and improved resolution of the images suggest new morphological boundaries which need to be reconciled with the mineralogical boundaries.

This project will make use of the latest visible and spectroscopic images to map individual flows based on morphological and mineralogical criteria. The project will focus on the western basalts of Oceanus Procellarum, and will build on previous work performed on Mare Imbrium (Thiessen, Besse et al., 2015, PSS, 104, P.244-252). The outcome of the project will be a map of the lava flows boundaries based on scientific analyses of the mineralogy and morphology. These boundaries will serve as a basis to evaluate the age of the units based on Crater Counting Statistics, using the newest improvements developed over the past years in age modelling techniques. The work will be performed at ESAC/Spain, in strong collaboration with experts located in ESTEC/Netherlands.

Figure: Extracted from Thiessen et al., 2015. Boundaries of the Imbrium basin lava flows extracted from visible to near-infrared mineralogy.

Project duration: 6 months

Desirable expertise or programming language:

  • Interest in planetary science and scientific research is required
  • Generic knowledge of geological/morphological processes is preferable
  • Knowledge of Geographical Information System (GIS) and/or programming language (e.g. Python) will be needed
  • Fluency in English is required
  • • Interest in data mining and data visualisation would be beneficial

To apply for this project please go here.


ESAC supervisor(s): Norbert Schartel, Richard Saxton, Maria Santos-Lleo, Laura Manduchi, Felix Fuerst

XMM-Newton is an ESA space observatory that collects X-rays from astronomical sources. There are about 280,000 sources in the XMM-Newton Source Catalogue which have X-ray spectra. The majority of them were previously unknown and hence their intrinsic nature and physical origin are also unknown. The  sual X-ray spectral  analysis technique involves a fitting process which requires making  hypothesis about their physical nature and comparing the predictions with the observed spectra. This makes it very problematic  to handle a large number of spectra. 
In 2019 Laura Manduchi developed a neutral network architecture for the analysis of XMM-Newton spectra. First tests showed remarkable success and, especially, that the neutral network is superior to the fitting process. Her code together with her report with detailed description of the results are available at ESAC for a continuation of the so far successful project. 

The successful applicant will train the neural network with new  and more complex simulated spectra. Examples of such models are  those that describe the spectra of general relativistic  accretion disks around Kerr black holes. After the network is trained,  the student will process all the X-ray spectra in the XMM-Newton Catalogue with the trained neural network with the aim to identify  supermassive black holes and characterise their physical parameters.  The results will be documented on a technical report or in a paper  to be submitted to the refereed literature, pending on the work  outcome. The intention is to also make the code  publicly available on a GitHub repository or a similar. 

Basic experience in:
- Scientific  research
- Analysis of data from space observatories, like XMM-Newton,
- X-ray astrophysics, including the production of simulated X-ray spectra from adopted physical models with different parameters
- Artificial neural networks
- Training, testing and analysing results of artificial neural networks

Potential for a Master Research Project.
Potential to be published as an XMM-Newton technical note and/or paper in the scientific literature.

Project duration: 3-6 months

Desirable expertise or programming language:

  • Physics, Astrophysics, Mathematics, Data Science or Software Engineer career paths are appropriate to opt to this project.
  • Previous experience with machine learning-based tools, like artificial neural networks would be very advantageous.

In addition one or more of the expertise listed below are desirable and will be considered in the selection, but they are not required:

  • Basic Astrophysical background, or Basic Data Science knowledge or Software Engineering background
  • Linux at user level, programming languages like  Python or IDL,
  • XMM-Newton software (SAS) or with standard routines in X-ray astronomy like xspec or with astronomical databases (ADS, NED, Vizier ...)

To apply for this project please go here.


ESAC supervisor(s): Maria Garcia Reinaldos, Luis Martin, Jose Marcos, Ruben Perez

ESAC PA has implemented a Product Assurance Environment used to perform analysis of different project processes and products using tools replicated from the projects. This environment is connected to a PA DB management system that allows dynamic access to metrics of SCI-S projects supported by the PA team. The objective of this activity is to go one step further by analysing metrics from SCI-S projects and identified correlations between poor metrics values and potential project risks. The trainnee shall design different dashboards using the PA DB and analyse the correlations between poor value metrics and project performance trends in order to identify common nocive patterns among the SCI-S projects and to provide general recommendations to avoid them.

Project duration: 6 months

Desirable expertise or programming language:

  • Software engineering background.
  • Basic Knowledge of Software Quality Assurance practices and techniques.
  • Basic knowledge of programming languages (e.g. java, python, HTML)
  • Knowledege of SW engineering products and processes metrics
  • Knowledge of some software quality tools (e.g. SonarQube, findBugs, PMD) 
  • Analytical skills
  • Good communication skills

To apply for this project please go here.


ESAC supervisor(s): Eva Verdugo, Ivan Valtchanov, Maria Santos-Lleo, Pedro Rodriguez, Maggie Lieu, Antonia Vojtekova, Hector Rueda

Significant advances in restoration, de-noising and increasing the resolution of images are possible with machine learning (ML) techniques. There are methods based on convolutional neural networks (CNNs) with significant success at de-noising photographs obtained at non-optimal conditions (low light, short exposure time):  the Learn to See in the Dark project. Successful application of ML methods achieved an increase of the resolution of digitally zoomed photographs:  Zoom to Learn, Learn to Zoom project. During the ESAC traineeship in 2019, the "Learn to See in the Dark" approach was successfully implemented for astronomical images from the Hubble Space Telescope, achieving a factor of 1.3-1.5 in terms of signal-to-noise ratio for CNN produced de-noised images (Vojteková et al, 2020).

The goal of this project will be to use these ideas and to try and develop ML-based methods for super-resolution and de-noising of X-ray images from ESA's XMM-Newton space telescope. The X-ray images are in Poisson noise regime and have different properties in comparison with the images from optical telescopes, like the Hubble Space Telescope. The X-ray photons are recorded with their arrival time, position on the detector and their energy, so light-curves (i.e. the number of photons per time interval during the observation) and images in different spectral bands can be constructed. Incorporating these characteristics as inputs to the network and then deciding on the best network architecture will be challenging. The trained network could be used to improve the quality of the XMM-Newton images, both in terms of noise properties and higher spatial resolution for features in extended sources.  As the XMM-Newton Science Archive contains observations spanning about 20 years, the amount of data is a goldmine for such projects. At the end of the day, improving the quality of the already available data is also of a great interest for the astronomical community and for the lasting legacy of the XMM-Newton.

The trainee will follow the ideas in "Learn to See in the Dark" and in "Zoom to Learn, Learn to Zoom" and implement, train, test and validate a neural network with suitable architecture to allow for inputs based on the characteristics of the XMM-Newton images.

The trainee will gain experience of research in astrophysics, use of science archives X-ray observations and data products, will learn or improve their knowledge and practical skills on using different machine learning frameworks.

Duration: 6 months

Desirable expertise or programming language:

  • Python, Tensorflow or other framework (desirable)
  • Background or interest in astronomy and astronomical observations.

To apply for this project please go here.


ESAC supervisor(s): Catarina Alves de Oliveira, Pierre Ferruit

NIRSpec is the work-horse spectrograph on board the James Webb Space Telescope to be launched in 2021 (https://www.jwst.nasa.gov/ and https://sci.esa.int/web/jwst). The purpose of the traineeship is to use the NIRSpec Instrument Performance Simulator software to perform simulations of the type of ground-breaking observations that NIRSpec will carry out once the telescope is in space, for example in the field of star formation. The simulations will be made available to the scientific community. They are of crucial help to scientists to master the use of this powerful, yet complex, instrument and to be ready to exploit the data as soon as they are acquired.

The activities of this internship will focus around preparing the inputs to the NIRSpec Instrument Performance Simulator and generating simulated exposures. This will require understanding the properties of the astronomical field to be 'observed' and updating or developing scripts to prepare the inputs to NIRSpec Instrument Performance Simulator. The simulated exposures will need to be inspected, packaged for delivery and described in an accompanying document. 

Thanks to these activities the trainee will learn about the many aspects that enter an astronomical observation: the sources, the instrument configuration, pointing, exposure times, data types and formats, while gaining familiarity in writing scientific communications and increasing their experience with computer programming.

Time-permitting, the trainee will expand the project by analysing the simulated spectral data to retrieve scientific parameters used in the characterisation of young stellar objects, and the star forming regions they are embedded in. This will provide added experience in research in astrophysics.

Project Duration: 3-6 months

Desirable expertise or programming language:

  • Some background in astronomy and foundation knowledge of spectroscopy
  • Knowledge of the Python programming language is an asset

To apply for this project please go here.


ESAC supervisor(s): Nils Janitzek, Andrew Walsh

Solar Orbiter (SOLO), launched in February 2020, is the most recent ESA/NASA science mission to study the Sun and the heliosphere. Already during the current cruise phase the SOLO spacecraft will approach the Sun as close as 0.5 AU and therefore allow unprecedented measurements of the solar activity in the inner heliosphere with a comprehensive suite of in-situ instruments.
Eruptions on the Sun, that release large amounts of plasma from the solar atmosphere can lead to the formation of plasma shocks that travel through interplanetary space and efficiently accelerate particles to high energies. In order to enable predictions of the intensity of such energetic particle events, that can cause radiation hazards for spacecraft and humans in space, we have at ESAC an ongoing study that focuses on the a quantitative understanding of the injection and acceleration process of particles at interplanetary shocks.

The trainee will support this investigation by identifying and characterizing shocks in the SOLO in-situ data based on measurements of the solar wind bulk plasma and magnetic field with the SOLO MAG and SWA instruments. In particular, the trainee will analyze occurring shocks for their dynamic properties, such as their compression ratio and their orientation with respect to the ambient magnetic field, but also identify relations to the initial solar eruptive events from onset-time and plasma composition measurements. In case of a successful traineeship the trainee will be fully integrated in the publication process of the related common studies on particle shock acceleration.      

Project duration: 3-6 months

Desirable expertise or programming language:

  • The applicants should be in their final years of a university course at masters level (or equivalent) in physics, astrophysics or space science.
  • Experience with interplanetary in-situ particle and/or magnetic field measurements
  • Experience  with a scientific programming language, preferably python including numpy and scipy, knowledge of other space-related (python) programming tools is an asset
  • High interest in scientific space data analysis in the context of solar physics and the Solar Orbiter mission, and a high motivation to learn about and apply new data analysis methods

To apply for this project please go here.


ESAC supervisor(s): Pedro García-Lario, Jesús Maíz Apellániz

The advent of large-scale photometric and astrometric surveys has opened the way to the study of the  Galactic stellar population beyond 1 kpc, where photometry is dominated by the effect of extinction instead of the intrinsic spectral energy distribution of the stars, making all stars “red". At large distances the majority of the observed population in those surveys is composed of bright red giants but among them we also find obscured OB  stars, among other interesting objects, like e.g. AGB stars. Finding those needles in the haystack is the purpose of this traineeship. The trainee will combine Gaia DR2 photometry and astrometry with optical photometry from GALANTE, which is surveying the Northern Galactic Plane using the T80 telescope at Javalambre, Teruel, Spain. He/she will characterize the obscured population of the Galactic Plane, identify the different types of objects, and measure their extinction. This work is suitable to be considered as a research master project.

Project duration: 6 months

Desirable expertise or programming language:

  • Knowledge of Python and/or IDL,
  • Use of astronomical databases and associated tools (Aladin, Topcat),
  • Previous experience with photometry is a plus.

To apply for this project please go here.


ESAC supervisor(s): William Alston, Andrew Lobban, Michael Parker, Maria Santos-Lleo, Gabriele Matzeu

Accreting black holes are unique sources of some of the most extreme physics in the universe. They are a testbed for our understanding of general relativity (GR) and high-energy physics, and play a vital role in shaping the cosmos from its origins to how it appears today. The accretion process is not static, so the application of time-series analysis (signal processing) methods provides important insight that spectra or spatial information cannot achieve alone.
X-ray reverberation echoes are subtle time-delay signatures observed in active galaxies, which allow us to spatially map the unresolved accretion geometry around the black hole. Being able to measure the geometry then allows us to make an independent estimate on black hole mass and spin, as well as revealing the detailed physics involved when matter is accreted. 

The selected candidate will build on our recent work in applying existing full GR models to the observed reverberation signatures.  This will require a combination of applying time series analysis methods to X-ray data as well as statistical hypothesis testing when applying models to the data. This will be performed on a sample of black hole sources. It is expected the project will lead to a publishable research article.

Suggested reading:
Alston et al (2020) Nature Astronomy: https://www.nature.com/articles/s41550-019-1002-x

Project duration: 4-6 months

Desirable expertise or programming language:

  • Basic astrophysics background
  • Basic understanding of statistics
  • Familiarity with IDL, Python or R programming languages.
  • Familiarity with Linux operating systems

To apply for this project please go here.


ESAC supervisor(s): Bruno Merín, Beatriz Martínez, Elena Colomo and Isa Barbarisi

In the context of the continuous improvement of the Science Data Archives provided by the ESAC Science Data Centre (ESDC) to the scientific community and to the users in general, the software engineers in the team have identified areas in the development and operations of the software projects where automation and streamlining of the deployment system would enormously reduce the time-to-data  for the scientific users of our archives and therefore maximise the scientific return of the scientific data hosted inside the archives. These improvements are being introduced into the development and operational workflows by adopting DevOps practices. The trainee will first work with the ESDC engineers to finalise the deployment of a real-time web-based map of accesses to the different archives to be added to the ESDC web-site at the end of the project (such as the one in the figure showing accesses at the Gaia Data Release #2 in April 2018).

DevOps is now a relatively well-established set of practices based around Continuous Integration and Continuous Deployment (CI/CD) and infrastructure. DevOps practitioners put tools and processes in place to realise faster time to value and greater governance for software development projects.

The selected trainee will then contribute to the deployment and installation of a DevOps infrastructure in the ESDC and will where possible contribute their earlier experience in DevOps in other contexts. It is of particular interest as well to start to apply new MLOps techniques where Machine Learning models are folded into the decision making for operational workflows based on real-time automated analysis of the operational data, such as the near-real-time user inflow as described above.
The ESDC currently contains very experienced full-stack software engineers in many technologies as well as very experienced scientists in Astronomy, Planetary Science and Heliophysics who work collaboratively to provide innovative data services to the scientific communities that they serve. The trainee therefore will have plenty of sources of learning at many different levels and will enjoy a purely international and highly professional work environment.    

Project duration: 6 months

Desirable expertise or programming language:

  •  Experience with Java and Java Script programming languages is required
  •  Experience in DevOps is required and knowledge of MLOps in an operational setting would be an asset
  •  Knowledge of at least one Java Script framework (Angular, React, Vue or Vaadin) is required
  •  Knowledge of Postgres, Oracle and parallel databases would be an asset
  •  Interest in space science is a great asset

To apply for this project please go here.


ESAC supervisor(s): Maria Santos-Lleo, Jose Vicente Perea, Pedro Rodríguez   

The X-ray Multi Mirror (XMM-Newton) observatory is one of the lead science missions of the European Space Agency (ESA). This space platform is able to perform simultaneous pointing observations with different instruments. Among these instruments, a set of three X-ray CCD cameras called the European Photon Imaging Camera (EPIC). X-ray data events collected with the EPIC cameras are processed at the XMM-Newton Science Operations Centre following a strict set of algorithms and calibration methods. The main focus of this internship experience is to explore the same X-ray data by using machine learning techniques. The exploration of the data with these machine learning algorithms might yield a new vision of the instrument characterization or even find some patterns unnoticed from the regular processing methods. Machine Learning clustering methods have been shown to be suitable to manage multidimensional data. Because the X-ray events collected by the EPIC cameras are registered as multidimensional arrays we have selected clustering algorithms to explore the data. The successful applicant will be able to interact with other interns working on similar projects at ESAC also offered in this call. 

Duration: 3-6 months

Desirable expertise or programming language:

  • Python programming, 
  • Machine Learning clustering algorithms,
  • XMM-Newton European Photon Imaging Camera (EPIC) data processing,
  • Astronomical X-ray detection basics.

To apply for this project please go here.


ESAC supervisor(s): Xavier Dupac, Julio Gallegos, Fernando Martin

For the past 4 years, we have developed several Cubesat subsystems:  telecommunications, structure, AOCS, power system and the on-board computer and data handling.  It is time to integrate all of them for further testing.  Your job will be to fit all the subsystems for  a 1U and 3U Cubesat.  The AOCS is particularly challenging as it is a combination of magneto-torquers and 3 (1U cubesat) or 4 (3U) reaction wheels.

Once the system is integrated you will have to determine and balance it center of mass to meet the Cubesat standard and perform the system tests.

Project Duration: 6 months

Desirable expertise or programming language:

  • Very extensive knowledge  of C++ and microprocessors like TI CC3200
  • Knowledge of CCSDS and communication protocols
  • Matlab and experience with cubesats is a plus

To apply for this project please go here.

To see  trainee projects 2021 please click here.