Tutorials for Nexus - Nuclear Elastic X-ray scattering Universal Software
Tutorials for the software package Nexus. The Nuclear Elastic X-ray scattering Universal Software (Nexus) is a Python package for simulating and fitting of Moessbauer spectra, nuclear resonant scattering (NRS) data, pure electronic X-ray reflectivities (XRR), nuclear X-ray reflectivities (nXRR),...
Scientific topics: nuclear resonant scattering, resonant scattering
Tutorials for Nexus - Nuclear Elastic X-ray scattering Universal Software
https://fs-mcp.pages.desy.de/nuclear-nexus/tutorial/tutorial.html
https://pan-training.eu/materials/tutorials-for-nexus-nuclear-elastic-x-ray-scattering-universal-software
Tutorials for the software package Nexus. The Nuclear Elastic X-ray scattering Universal Software (Nexus) is a Python package for simulating and fitting of Moessbauer spectra, nuclear resonant scattering (NRS) data, pure electronic X-ray reflectivities (XRR), nuclear X-ray reflectivities (nXRR), and polarization dependent electronic scattering.
nuclear resonant scattering
resonant scattering
A deep dive into the mathematics of Machine Learning
Crash course on Machine Learning
Keywords: machine learning
Resource type: slides
A deep dive into the mathematics of Machine Learning
https://indico.desy.de/event/33127/contributions/117087/attachments/71317/91043/user_meeting_ml_intro_jan_2022.pdf
https://pan-training.eu/materials/a-deep-dive-into-the-mathematics-of-machine-learning
Crash course on Machine Learning
machine learning
research data scientist
masters students
PhD students
Jupyter notebooks on Machine Learning for scientific data analysis
Jupyter Notebooks serving as supplementary material for a tutorial on Machine Learning, originally presented at the 2022 European XFEL user meeting.
Resource type: jupyter notebook
Jupyter notebooks on Machine Learning for scientific data analysis
https://git.xfel.eu/danilo/ml-tutorial
https://pan-training.eu/materials/jupyter-notebooks-on-machine-learning-for-scientific-data-analysis
Jupyter Notebooks serving as supplementary material for a tutorial on Machine Learning, originally presented at the 2022 European XFEL user meeting.
research data scientist
PhD students
masters students