We are looking for a highly motivated and talented researcher to work on the development and deployment of tools that allow users of MAX IV, the European Spallation Source (ESS) as well as the Chalmers Materials Analysis Laboratory (CMAL) to take advantage of advanced statistical learning techniques for design and interpretation of their diffraction experiments. The project comprises two main components: (i) the application of Bayesian statistics for analyzing diffraction patterns, specifically crystallographic model selection and parameter sensitivity quantification, and (ii) the use of Bayesian optimization for finding instrument settings (and instruments) by maximizing the sensitivity and contrast between several different crystallographic models. These tools will both aid the optimal utilization of these resources and crucially improve the quality of the analysis of experimental data, providing added value to both the infrastructures and their users.

You will work in a team comprising researchers from the Data Management and Software Center of the European Spallation Source (ESS) in Copenhagen, the MAX IV synchrotron radiation facility in Lund, and the Department of Physics at Chalmers University of Technology in Gothenburg. Your main posting will be be hosted at Chalmers under the supervision of Professor Paul Erhart at the Condensed Matter and Materials Theory (CMM) division but will included extended stays at the ESS and MAX IV. The CMM division comprises about 20 researchers, who combine theoretical, methodological and computational techniques, often in close collaboration with local and international experimental groups. The work in the division is characterized by a strong interplay between fundamental and applied research and this allows us to address pressing societal issues, e.g., related to sustainable energy or information technology.

Major responsibilities
At the core part of this project you will employ Bayesian statistics to generate physically informed crystallographic models from diffraction data, including a sensitivity analysis. Working in a team, your role will be to design and implement a framework for this task and to apply it for the analysis of prototypical data sets provided by our experimental collaborators. In this process you will need to utilize a number of different Python packages and integrate them into a stable and extensible structure. Your work will contribute to and build on the easydiffraction environment (https://easydiffraction.org/), which provides both Python and graphical user interfaces to established diffraction libraries.

This project will enable you to deepen your understanding of physics and materials science by utilizing machine learning techniques in application oriented research. It will also allow you to expand your knowledge of state-of-the-art software and data engineering techniques in an academic environment. Moreover, you will be able to work in a cross-disciplinary, collaborative environment and have ample opportunities to grow your professional network.

Qualifications
You should hold a PhD degree in physics, chemistry, computer science or a related discipline and have a strong interest in computational modeling  of materials with expertise in at least three of the following areas:

• X-ray and/or neutron diffraction techniques
• machine/statistical learning
• Bayesian statistics
• code development in Python
• software engineering

You should enjoy working in a collaborative environment including interactions with both theoreticians and experimentalists.

Please submit your application via the Chalmers job portal, where you can also find additional information regarding this position.