It is now two months ago that I started my Postdoc position at the Jülich Centre for Neutron Science (JCNS). JCNS is an institute of the Forschungszentrum Jülich which itself is part of the Helmholtz association. More concretely, I am working in the Scientific Computing group of the JCNS-4 outstation at the FRM II which is the TUM neutron source.
I was hired to contribute to the project AINX (Artificial Intelligence for Neutron and X-ray scattering) which investigates machine learning techniques on their use for neutron and X-ray scattering experiments.
The project is divided into two main phases.
Phase 1: Together with instrument scientists for the triple-axes spectrometer PANDA (Twitter: @PandaMlz), my principal investigator Dr. Marina Ganeva and myself try to guide corresponding experiments by using Gaussian Process regression. Gaussian processes are capable of quantifying uncertainties in function approximation and, hence, they can provide reasonable suggestions for informative measurements locations, namely that with highest uncertainty.
Phase 2: Many neutron experiments are disrupted by unfavorable artifacts like noise or background signals, spurious peaks, and others. We aim at training neural networks in that they will be able to uncover informative data by removing the mentioned disruptions. More details need to be figured out when it comes to implement this plan.
I am looking forward to all the new things I can learn and accomplish in the next time. Especially, the highly interdisciplinary flavor of this project, working in a team with scientists having various backgrounds, will be interesting and fun.