The tremendous progress of the last 20 years in the domain of laser-plasma accelerators has allowed us to understand the physics of the acceleration process and to optimize the properties of the electron beams working on each different step of the process, from the laser to the electron beam itself. One of the next steps in the field is to develop such Laser-driven Particle Accelerators (LPA) for applications. 

Since the first, very recent, experimental demonstration of the huge potential of machine learning techniques to automate and control laser wakefield accelerators, their use in the field has been growing exponentially. Various algorithms are developed and tested to efficiently determine the optimum parameter set that will lead to the ideal laser–driven electron beam for a specific application. Among applications, the LPA, due to their extremely short duration (femtosecond range), are extreme dose rate sources of great interest for radiotherapy, especially in the context of FLASH radiotherapy. Since the first experimental evidence for FLASH effect in 2014 with conventional accelerators, the worldwide community is actively working on trying to understand the basic mechanisms underlying the physical and biological process responsible for the increased preservation of healthy tissue surrounding the tumor treated with ultrashort electron bunches. LPA is a promising alternative source to conventional accelerators for investigating the fundamental physico-chemical processes underlying these biological effects. 

The Physics at High Intensity group at LIDYL has strong expertise in LPA, both from the experimental and numerical point of view. Benefiting from a brand new experimental facility equipped with a 100TW class laser system, we have implemented numerous diagnostics along the laser beam transport from the laser area up to the experimental chamber to be able to control the laser and the plasma. The next step is to implement Machine Learning techniques for optimizing the laser-driven electron accelerator to ultimately irradiate samples (biological, chemical …). As part of the EURO-LABS network (European Laboratories for Accelerator Based Sciences), we will benefit from a ML toolkit, developed by GSI (Germany). This tool is conceived for conventional accelerators, and it will have to be adapted to the specificities of LPA and tested on our experimental facility for validation. 

This project will benefit from strong support of numerical simulation experts from the PHI group (H. Vincenti and coworkers).

A fully funded one year position, in the context of EURO-LABS project (European Laboratories for Accelerator Based Sciences – under grant agreement Grant Agreement No: 101057511). Extensions could be considered depending on funding availability.

The starting date will be around September 2023.

The ideal candidate for this position will hold a PhD (before 09/2023), preferably in physics or related disciplines. 

The candidate must have strong skills and interests in one or more of the following areas: 

-laser-plasma acceleration

-laser-solid interaction at high intensity

-Machine Learning techniques

Good knowledge of the Python programming language is essential.

Candidates must apply online via the CEA career website via (https://www.emploi.cea.fr/offre-de-emploi/liste-offres.aspx) and by additionally emailing a complete file to jobs.lidyl@cea.fr.

The application file will contain: 

-         a CV;

-         a comprehensive record of professional achievements (publications, fellowships, awards, etc…) including a short description of the main personal accomplishments (conceptual, technical, …);

-         a cover letter highlighting the motivations for applying to this position;

-         contact information for 2 references that might be contacted.

Applications will be accepted until the position is filled.

Contact: jobs.lidyl@cea.fr.

For more details about the position, please write to sandrine.dobosz@cea.fr.