Key task:- development and implementation of new (unsupervised) machine learning algorithms for the classification of brain signals (EEG) in real-time- data preprocessing, statistical analysis and visualization with Python (Pandas, Scipy, Matplotlib) and Matlab- working closely together with medical specialists and patients to design and test the first language training for patients with aphasia- teaching-assistant for the machine learning course (>150 students) and permanent supervision of several Master students- scientific dissemination (see below for a summary) and writing grant proposals[1] Hübner, D., et al. "Learning from label proportions in brain-computer interfaces: online unsupervised learning with guarantees." PLoS One (2017) https://doi.org/10.1371/journal.pone.0175856[2] Verhoeven, T., Hübner, D., Tangermann, M., Müller, K. R., Dambre, J., & Kindermans, P. J. (2017). Improving zero-training brain-computer interfaces by mixing model estimators. Journal of neural engineering, 14(3), 036021.[3] Hübner D, Verhoeven T and Müller K-R, Kindermans P-J and Tangermann M (2018), "Unsupervised Learning for Brain-Computer Interfaces Based on Event-Related Potentials: Review and Online Comparison", IEEE Computational Intelligence Magazine. Vol. 13(2), pp. 66-77. IEEE.Updated 9/2019.