Emergent AI Center welcomes eight new projects

The Emergent AI Center welcomes the following new projects:

  • Neural network-based approaches for multiscale modelling of topological defects
    Prof. Dr. Friederike Schmid, Dr. Karin Everschor-Sitte
  • Machine learning prediction of Odorant Receptor (OR)-ligand interactions: Chemical communication as the basis of swarm intelligence
    Prof. Dr. Miguel Andrade, Prof. Dr. Susanne Foitzik
  • Computational performance of autistic-like networks
    Prof. Dr. Martin Heine, Dr. Arthur F Bikbaev, Jun-Prof. Dr. Susanne Gerber
  • How emergent is the Brain?
    Prof. Dr. Heiko Luhmann, Jun-Prof. Dr. Maik Stüttgen, Prof. Dr. Illia Horenko
  • Application of machine learning algorithms to search for dark-matter signals in spectroscopic data
    Prof. Dr. Dmitry Budker, Prof. Dr. Stefan Kramer
  • Deep Learning of natural selection in population genomic data
    Dr. Yoan Diekmann, Prof. Dr. Joachim Burger
  • Repetitive DNA and the ‘C-value enigma’ of genomics
    Prof. Dr. Thomas Hankeln, Prof. Dr. Miguel Andrade
  • Learned Structures for Big Data in Bioinformatics
    Prof. Dr. Bertil Schmidt, Prof. Dr. Thomas Hankeln, Prof. Dr. Andreas Hildebrandt

For more information on the individual projects, please take a look at the research part of the website

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