PostDoc position – Machine Learning and nanosats to probe the interior of Solar System bodies

Duration: 2 years


The study of asteroids is fundamental both for our understanding of the formation of the Solar System, or the supply of water and origin of life on a planet, and for the prediction of Earth impacts. Knowledge of the structure of small bodies (from a few meters to several hundreds of km) is an important element. Indeed the mass, the density, and the internal structure of the small bodies are as many key factors to understand their formation, and diversity, and tracing the origins of planetary systems in general. Moreover, making the link between the internal structure of small bodies and their external shape is the next major challenge in the field.

Nanosatellites or cubesats offer a new opportunity to perform gravity field determinations that we are developing within the BIRDY project. These local relative techniques and measurements show an innovative aspect for this type of interplanetary missions. In particular, we want to study the radio-science and POD precise orbit determination technique, considering and exploiting all the possibilities offered by inter-satellites links (ISL) radio links between one or more nanosatellites. This, in order to derive the lower order of the gravitational potential, mass, bulk density, mass distribution, etc. Our study will develop the concept of radio/optical measurements through inter-satellite links, within the BIRDY project, in the event of a reconnaissance mission (planetary defence, fly-by) or an exploration mission (planetary science, rendez-vous).

Moreover, the next challenge we want to tackle is to relate the external morphology of small bodies – modelled as gravitational aggregates – to their internal structure. We will develop in particular machine-learning ANN/PINN methodologies for inverse problems in the determination of gravity fields and tomography, as well as the modelling of the internal structure of gravitational aggregates with SSDEM numerical methods.


Three research axis will be covered with this post-doc work:

  • Develop precise orbit determination process to probe the gravity field through ISL (radio/optical). Analyse a space mission to asteroid Aophis close encounter in 2029. Derive an optimisation analysis for the measurements and nanosatellites configuration, assess the need for complementary ground-based measurements. Validate the approach with our RF-test bench deployed at CENSUS.
  • Develop a gravity-and-tomography global inversion algorithm, using artificial neural networks, to derive the internal structure of small bodies. This will be done by combining complementary radio-science (gravity) and LFR radar (tomography) observations.
  • Model granular systems using SSDEM numerical simulations, to provide a link between the external morphology of a body with it’s modelled interior.

The work will be performed at Paris observatory within IMCCE and CENSUS space centre. The methodology will be applied to several targets of interest, and in particular to the HERA mission from ESA.

Izzo & Gomez 2022 Commun. Eng. 1, 48
Martin et al. 2002 CMDA 134, 46
Hestroffer et al. 2021 7thPDC, #149
Hestroffer et al. 2019, AARv 27, 6

Candidate profile

  • PhD in computer science, machine learning, mathematics, A&A, data scientist, or equivalent
  • Experience in neural networks and regression
  • Strong skills in programming and numerical computations
  • Excellent written and verbal communication in English

Training and skills required

  • Advanced skills in data analysis and the use of statistical software
  • Very good understanding of Machine Learning theory and techniques
  • Strong experience with machine learning methods (ANN, PINN, …) for regression and inverse problems
  • Knowledge of gravitational field representation, and precise orbit determination appreciated
  • Knowledge of SSDEM numerical computations for granular systems is a plus



Employment address

Paris observatory
77 av. Denfert-Rochereau
75014 Paris