PhD student ENGAGE 05

Context & Job description

Thesis Subject: Machine Learning for the Real-Time Analysis of X-Ray Spectroscopy Data

X-ray spectroscopy is an indispensable tool for element-specific analysis in most fields of natural sciences. It gives access to a wealth of information regarding the structural parameters and electronic properties of materials.

Currently, mapping the experimental data to the various properties of materials relies on manual processing, i.e., every single spectrum is interactively analyzed by a scientist. However, with the ever-increasing acquisition rates allowed by the emergence of extremely brilliant light sources, such workflows will become unpractical.

You will join the Algorithms & Scientific Data Analysis (ADA) group and develop machine-learning techniques to make fast and accurate predictions of properties (e.g., coordination number, oxidation state, concentration, etc.) from X-ray spectroscopic data. Your objectives will be to:

  • build large datasets of theoretical calculations and experimental data that will be used to train machine learning models,
  • set up and evaluate the performance of various machine learning models,
  • design and implement an autonomous experimental acquisition pipeline that leverages the predictions made using these models,
  • apply the automated pipelines to characterise catalytic systems, g., Cu-zeolites, Pt nanoparticles.

The project will bring much-needed automation of the data analysis workflows for X-ray spectroscopy, enable non-expert users to make better use of their data, and make the techniques more accessible to an even larger scientific community and especially industry. All work will be carried out in close collaboration with the ESRF spectroscopy beamlines, in particular ID24 and ID26.

Further information may be obtained from Dr. Marius Retegan (tel.: +33 (0)4 76 88 19 29, email:




Expected profile

  • Degree allowing enrolment for a PhD (such as MSc, Master 2 de Recherche, Laurea or equivalent) in physics, chemistry, computational science, or a related field.
  • Knowledge of computational chemistry/physics software.
  • Proficiency in English (working language at the ESRF)
  • Compliance with the Marie Sklodowska-Curie mobility rule: candidates must not have resided or carried out their main activity (work, studies, etc.) in France for more than twelve months in the three years immediately before the date of recruitment
  • Eligibility: Early-Stage Researchers (ESRs) shall, at the time of recruitment by the host organisation, be in the first four years (full-time equivalent research experience) of their research careers and have not been awarded a doctoral degree

Working conditions

The successful candidate will be enrolled at the Université Grenoble-Alpes. The candidate will be hired by the ESRF (Grenoble, France). The contract is of two years renewable (subject to satisfactory progress) for one year.

The ESRF is an equal opportunity employer and encourages diversity.

If you are interested in this position, please apply via the ENGAGE website (not on the ESRF website) by 15 March 2022


This PhD is co-funded by the Marie Skłodowska-Curie COFUND project ENGAGE (grant agreement101034267), and applicants must follow the associated rules notably regarding mobility (see In particular, applicants must not have resided or carried out their main activity (work, studies, etc.) in France for more than 12 months in the last 3 years.

Company description

The European Synchrotron, the ESRF, is an international research centre based in Grenoble, France.

Through its innovative engineering, pioneering scientific vision and a strong commitment from its 700 staff members, the ESRF is recognised as one of the top research facilities worldwide. Its particle accelerator produces intense X-ray beams that are used by thousands of scientists each year for experiments in diverse fields such as biology, medicine, environmental sciences, cultural heritage, materials science, and physics.

Supported by 21 countries, the ESRF is an equal opportunity employer and encourages diversity.

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