Young Graduate Trainee for Flight Dynamics Operations

The Flight Dynamics Division is responsible for providing mission analysis and flight dynamics operations support to ESA missions in the fields of orbit determination, manoeuvre optimisation, GNC monitoring and commanding. This includes several interplanetary missions (BepiColombo), astronomy missions at the Sun-Earth Lagrangian Points (Euclid) and Earth orbit missions (Sentinels fleet). The support covers the entire mission life cycle, comprising design, development, implementation and validation of SW tools, mission preparation and critical/routine operational activities proper.

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

Space debris poses a continuously increasing risk to operational Earth observation satellites that has made in-orbit collision risk assessment an integral part of routine operations.
This process requires as essential input the orbit state vector and associated accurate uncertainties from the flight dynamics system where orbit determination is performed through a least-squares fit of the dynamic model to observation data. However; often the resulting covariance matrix provides unrealistic uncertainties. This can be caused by un-modelled correlations between consecutive observations or missing considered parameters to take into account dynamic model errors.

During the Traineeship you will investigate how more realistic error values can be obtained.

First, this requires systematic assessment of the actual orbit determination uncertainties for multiple Earth observation satellites and typical orbit determination scenarios (high and low drag depending on solar activity and orbit determination with/without calibration of a manoeuvre) by comparison with precise orbit determination solutions or with those from different orbit determination arcs. Then, these uncertainties can be compared with covariance matrix data obtained from different orbit determination approaches which could be based on:

• the decrease in the signal-to-noise ratio of the measurement errors through the use of underestimated weights on the data to achieve error whitening
• or a reduction in the used observations for the covariance matrix calculation
• introducing additional covariance terms into the estimator to consider the model parameters errors

You should have just completed, or be in the final year of a university course at Master' level (or equivalent) in a technical or scientific discipline. In particular in engineering, physics or mathematics.

The education should have provided a solid knowledge of mathematics, classical mechanics, orbit mechanics, as well as C++/Python programming skills. Familiarity with estimation theory and open source publishing is an advantage.

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.

You should demonstrate good interpersonal skills and the capacity to work both independently and as part of a team.

During the interview your motivation and overall professional perspective/career goals will also be explored.