** training completed **
Welcome to our NMR Masterclass: AI techniques in biomolecular NMR spectroscopy (21 Feb 2024)
The next breakthrough in NMR is upon us! We are delighted to offer a 1-day class that focusses on the use of AI techniques in biomolecular NMR spectroscopy.
Artificial intelligence, machine and deep learning techniques in NMR are increasingly enhancing spectral interpretation & analysis, reducing (perhaps eliminating?) bottlenecks, and improving the accuracy of automated tasks. It is truly remarkable how 'problematic' NMR spectra can be significantly improved using these techniques.
Considered by many to be a serious game-changer in NMR, AI is likely to feature in everyone's research activities in the near future. Don't get left behind - this is a great opportunity to come and learn more from our UK-based experts and to contribute to these important advances!
Course organiser: Dr Sara Whittaker
This masterclass is aimed at intermediate+ level NMR spectroscopists (e.g. Year 2 and 3 PhD, Post-Docs, PIs & Research Technical Professionals (RTPs)) working on projects involving biomolecular characterisation and is intended to provide a platform from which students can interact with expert instructors and ask plenty of questions in a friendly environment. This class includes hands-on practical exercises (laptops required).
There will be ample time for Q&A. Anyone looking for a new perspective in NMR is warmly welcome to attend.
Pre-requisites: Exposure to intermediate or advanced level biomolecular NMR spectroscopy (including data analysis); hands-on spectrometer experience with macromolecules is advantageous. A desire to learn more about the benefits of AI in biomolecular NMR spectroscopy! Students should bring laptops capable of running NMRBox software for the practical exercises (please visit https://nmrbox.nmrhub.org to set up an account, laptops not supplied).
Programme Summary
Session 1: “Machine learning in NMR: how to build a training database”. Essentially, our wisdom on the fact that there is never enough real experimental data to train even the smallest net. Instructor: Professor Ilya Kuprov (Southampton)
Session 2: "Assessing the use of deep learning to reconstruct sparsely sampled spectra". Instructor: Professor D. Flemming Hansen (UCL)
We look forward to seeing you!