Dr. Martin Schüle

Arbeit an der ZHAW

Tätigkeit

Head Research AI & Computational Environment

Arbeits- und Forschungsschwerpunkte

• AI in Science and Environmental Sciences
• Artificial Life
• Natural Language Processing
• Philosophy of AI

Lehrtätigkeit

• Neural Networks and Deep Learning
• Advanced Deep Learning

Lehrtätigkeit in der Weiterbildung

• CAS in Digital Life Sciences
• Natural Language Processing Fundamentals

Netzwerk

Mitglied in Netzwerken

• Deutsche Physikalische Gesellschaft
• Italian Society for Chaos and Complexity
• Associate Researcher, Paris 1 Sorbonne, France

Projekte

• reasonAI – Erklärbares Schlussfolgern in grossen Sprachmodellen (LLMs) / Projektleiter:in / laufend
• Optimal Fertiliser Application / Projektleiter:in / laufend
• Maximizing the Benefits of Organic Fertilizers: A Data-Driven Approach to Improve Efficiency and Reduce Pollution / Projektleiter:in / abgeschlossen
• Investor and Stakeholder Tools for Tracking Companies’ Climate Commitments, Greenwashing and ESG Trends / Teammitglied / abgeschlossen
• Employing Natural Language Processing to identify inconsistencies in companies’ non-financial communication / Teammitglied / abgeschlossen
• An integrated modelling and learning framework for real-time online decision assistance in Swiss agriculture / Projektleiter:in / abgeschlossen
• Radiosands / Teammitglied / abgeschlossen
• A cloud-based IoT approach for food safety and quality prediction / Teammitglied / abgeschlossen
• Predicting investor behaviour in European bond markets through machine learning / Teammitglied / abgeschlossen
• Next Generation: Empfehlungssystem mit neuronaler Intelligenz / Teammitglied / abgeschlossen
• European government bond dynamics and stability policies: taming contagion risks / Teammitglied / abgeschlossen
• Efficient Urban Pluvial Flood Simulation / Projektleiter:in / abgeschlossen
• Monitoring der Lebensmitteltemperatur / Teammitglied / abgeschlossen
• Comprehensive Sales Forecasting for Supply Chain Optimization in Food Industry / Teammitglied / abgeschlossen
• Multi-Asset Investment Process using Bayes Ensembles of Trading Models / Teammitglied / abgeschlossen

Publikationen

Beiträge in wissenschaftlicher Zeitschrift, peer-reviewed

• Schüle, M. (2025). On the semantics of large language models. Intellectica, 81, 15–36.
• Schwendner, P., Schüle, M., & Hillebrand, M. (2019). Sentiment analysis of European bonds 2016 - 2018. Frontiers in Artificial Intelligence, 2(20). https://doi.org/10.3389/frai.2019.00020
• Schwendner, P., Schüle, M., Ott, T., & Hillebrand, M. (2015). European government bond dynamics and stability policies : taming contagion risks. Journal of Network Theory in Finance, 1(4), 1–25. https://doi.org/10.21314/JNTF.2015.012

Schriftliche Konferenzbeiträge, peer-reviewed

• Schüle, M. (2023). Learning to flash : a machine learning approach to synchronizing cellular automata [Conference paper]. 2023 International Symposium on Nonlinear Theory and Its Applications, 228. https://doi.org/10.34385/proc.76.B2L-16
• Gericke, E., & Schüle, M. (2023). Exploring neural cellular automata for simulating cellular interactions and synchronization phenomena [Conference paper]. 2023 International Symposium on Nonlinear Theory and Its Applications, 297–298. https://doi.org/10.34385/proc.76.B3L-14
• Schüle, M. (2020). The collaborative learning cellular automata density classification problem [Conference paper]. Proceedings of the 2020 International Symposium on Nonlinear Theory and Its Applications, 268.
• Uwate, Y., Schüle, M., Ott, T., & Noshio, Y. (2020). Echo state network with chaos noise for time series prediction [Conference paper]. Proceedings of the 2020 International Symposium on Nonlinear Theory and Its Applications, 274.
• Gygax, G., & Schüle, M. (2020). A hybrid deep learning approach for forecasting air temperature [Conference paper]. In F.-P. Schilling & T. Stadelmann (Eds.), Artificial Neural Networks in Pattern Recognition (pp. 235–246). Springer. https://doi.org/10.1007/978-3-030-58309-5_19
• Kaufmann, M., Schüle, M., Smits, T., & Pothier, J. (2020). Typing plasmids with distributed sequence representation [Conference paper]. In F.-P. Schilling & T. Stadelmann (Eds.), Artificial Neural Networks in Pattern Recognition (pp. 200–210). Springer. https://doi.org/10.1007/978-3-030-58309-5_16
• Ott, T., Schüle, M., Fellermann, H., & Uwate, Y. (2018). Structural evolution in networks of coupled maps with asymmetric influence amplification [Conference paper]. 2018 International Symposium on Nonlinear Theory and Its Applications (NOLTA2018), Tarragona, Spain, 2-6 September 2018, 546–549.
• Schüle, M., & Ott, T. (2018). Synchronization in cellular automata : the learning approach. 2018 International Symposium on Nonlinear Theory and Its Applications (NOLTA2018), Tarragona, Spain, 2-6 September 2018.
• Ott, T., Schüle, M., Held, J., Albert, C., & Stoop, R. (2016). Clustered multidimensional scaling with Rulkov neurons [Conference paper]. 2016 International Symposium on Nonlinear Theory and Its Applications, 389–392. https://doi.org/10.21256/zhaw-3532
• Schüle, M., Ott, T., & Schwendner, P. (2016). Forecasting correlation structures. Proceedings of the 2016 International Symposium on Nonlinear Theory and Its Applications.

Weitere Publikationen

Schüle, M. (2018). Introduction to artificial neural network theory : lecture notes.

Mündliche Konferenzbeiträge und Abstracts

• Schwendner, P., Schüle, M., & Hillebrand, M. (2019). Correlation influence networks for sentiment analysis in European sovereign bonds. Financial Revolution - Sentiment Analysis, AI and Machine Learning, London, United Kingdom, 25-26 June 2019.
• Schwendner, P., Schüle, M., Ott, T., & Hillebrand, M. (2018). Sentiment in European sovereign bonds. 3rd European COST Conference on Mathematics for Industry in Switzerland, Winterthur, 6 September 2018. https://www.zhaw.ch/storage/engineering/institute-zentren/iamp/sp_acss/Schwendner_20180906.pdf
• Schwendner, P., Schüle, M., & Hillebrand, M. (2018). Correlation influence networks for sentiment analysis in European sovereign bonds. Financial Revolution - Sentiment Analysis, AI and Machine Learning, Zürich, Switzerland, 30 October 2018.
• Ott, T., Glüge, S., Schüle, M., & Hill, C. (2018, June). A dynamic network approach for the analysis of pathogen transmission chains. The 26th Nonlinear Dynamics of Electronic Systems Conference, (NDES 2018), Acireale, 11-13 June 2018.
• Schüle, M., Ott, T., & Schwendner, P. (2018, June). Influence networks in financial markets : forecast scenarios. NDES 2018, 26th Nonlinear Dynamics of Electronic Systems Conference, Acireale, Italy, June, 11-13 2018.
• Schwendner, P., Schüle, M., & Hillebrand, M. (2017). Network analytics of sovereign bond dynamics. Frankfurt Summit on Network Analysis, Frankfurt, Germany, 26 October 2017.
• Schwendner, P., Schüle, M., & Hillebrand, M. (2017). Sovereign bond network dynamics. Mathfinance Conference, Frankfurt, Germany, 20-21 April 2017.
• Schüle, M., Ott, T., & Schwendner, P. (2017, June 6). Forecasting correlation structures. NDES 2017, 25th Nonlinear Dynamics of Electronic Systems Conference, Zernez, 5-7 June 2017. https://www.ini.uzh.ch/~lorimert/NDES2017/assets/NDES2017_programme_booklet.pdf
• Schüle, M., & Schwendner, P. (2016). European government bond dynamics and stability policies : taming contagion risks. 9th Financial Risks International Forum, Paris, France, 21 March 2016.
• Hillebrand, M., Ott, T., Schüle, M., & Schwendner, P. (2016). European government bond dynamics and stability policies. ADEMU Workshop on Risk-Sharing Mechanisms for the European Union, Fiesole, Italy, 20-21 May 2016.
• Schwendner, P., Schüle, M., & Hillebrand, M. (2015). European government bond dynamics and stability policies : taming contagion risks. Financial Risk and Network Theory, Cambridge, United Kingdom, 9 September 2015. https://www.jbs.cam.ac.uk/fileadmin/user_upload/research/centres/risk/downloads/150909_slides_schwendner.pdf

Zurück