We seek a highly motivated research scientist or postdoctoral researcher to develop machine learning techniques for estimating brain states from non-invasive neuroimaging data such as EEG and fMRI. Through such methodological development for neuro-technologies, we aim to contribute to mental healthcare in daily life and neuroscience. Examples of our current interest include (but not limited to):

  • Robust analysis techniques for multi-modal brain data (e.g. predicting fMRI network activity from EEG signals based on simultaneous EEG-fMRI recordings)
  • Hierarchical dynamical models for EEG data by extending the tractable neural network model for extracting EEG spatial features (Hirayama et al., ICML2017)
  • Statistical techniques for extracting and visualizing personal characteristics of brain functional networks (Hirayama et al., 2016; Takagi et al., NeuroImage 2019), and transfer learning for neuroimaging data from different users (Morioka et al., NeuroImage 2015)

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