"PhD student at ILL: studying Machine Learning for Neutron Reflectometry"

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

Thesis subject: Machine Learning for Neutron Reflectometry The aim of the PhD project is to provide machine learning (ML) based neutron reflectometry (NR) analysis as an automatized workflow for the reflectometry instruments at the Institut Laue-Langevin (ILL). The current ML models are optimized mainly for (monochromatic) X-ray reflectometry. We aim to generalize this approach to a wide range of samples and time-of-flight NR, coupled with automatic data reduction, to fully automatize the workflow at the ILL reflectometers. The integration of the ML module in the data acquisition and data processing ecosystem will enable real-time data analysis and will open the possibility for closed-loop experiments, where analysis results are used for feedback control in an ongoing experiment. As sample systems polymer thin films and protein layers will be used to test the developed analysis tools.

You will join the Large Scale Structures (LSS) group at the ILL, Grenoble, France and work mainly with the two NR machines D17 and FIGARO for the implementation of the ML tools. A total of 2-6 months will be spent at Tübingen University in Germany, mainly at the beginning of the project, in order to adapt the existing ML tools to the needs of this project. Further information may be obtained from: Dr. Philipp Gutfreund (gutfreund@ill.fr), or from Prof. Dr. Dr. h.c. Frank Schreiber (frank.schreiber@uni-tuebingen.de)

• A background in scattering techniques as well as programming would be an advantage.
• Degree allowing enrolment for a PhD (such as MSc, Master 2 de Recherche, Laurea or equivalent) in natural science like physics, chemistry, materials sciences, nanotechnology, etc.
• Proficiency in English (A proof of upper-intermediate B2 level must be included in the application. Applicants originating from native-English-speaking countries can apply without the need for proof of level. An official degree conducted in English will be also accepted as a proof). If the applicant is unable to provide proof, an English test (free of charge) will be requested.
• Compliance with the Marie Sklodowska-Curie actions mobility rule: candidates may not have resided or carried out their main activity (work, studies, etc.) in the host institute's country for more than twelve months in the three years immediately before the application deadline.
• The candidate must not already be working towards or in possession of a doctoral degree at the date of the recruitment, and must already hold a master's degree at the call deadline.
• Candidates must satisfy the conditions for enrolment in a doctoral programme.

The successful candidate will be enrolled in the doctoral school at Tübingen University (Germany) and based full-time at the ILL (Grenoble, France), other than at least two months of secondment to Tübingen University. Additional visits may be made to Tübingen University when needed. Furthermore, a varied pedagogical training programme will be offered to the successful candidate throughout the 3-year PhD project.

Location: 71 avenue des Martyrs Grenoble, France

Job type: PhD

Field: Machine Learning, Data Mining, Data Structures, Polymer Chemistry, Surface Chemistry, Materials Chemistry, Nanotechnology, Materials Physics