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), pp. 15–36.
• Schwendner, P., Schüle, M. and Hillebrand, M. (2019) 'Sentiment analysis of European bonds 2016 - 2018', Frontiers in Artificial Intelligence, 2(20). doi: 10.3389/frai.2019.00020.
• Schwendner, P. et al. (2015) 'European government bond dynamics and stability policies : taming contagion risks', Journal of Network Theory in Finance, 1(4), pp. 1–25. doi: 10.21314/JNTF.2015.012.

Schriftliche Konferenzbeiträge, peer-reviewed

• Schüle, M. (2023) 'Learning to flash : a machine learning approach to synchronizing cellular automata', in 2023 International Symposium on Nonlinear Theory and Its Applications. The Institute of Electronics, Information and Communication Engineers, p. 228. doi: 10.34385/proc.76.B2L-16.
• Gericke, E. and Schüle, M. (2023) 'Exploring neural cellular automata for simulating cellular interactions and synchronization phenomena', in 2023 International Symposium on Nonlinear Theory and Its Applications. The Institute of Electronics, Information and Communication Engineers, pp. 297–298. doi: 10.34385/proc.76.B3L-14.
• Uwate, Y. et al. (2020) 'Echo state network with chaos noise for time series prediction', in Proceedings of the 2020 International Symposium on Nonlinear Theory and its Applications, p. 274.
• Schüle, M. (2020) 'The collaborative learning cellular automata density classification problem', in Proceedings of the 2020 International Symposium on Nonlinear Theory and its Applications, p. 268.
• Kaufmann, M. et al. (2020) 'Typing plasmids with distributed sequence representation', in Schilling, F.-P. and Stadelmann, T. (eds) Artificial Neural Networks in Pattern Recognition. Cham: Springer, pp. 200–210. doi: 10.1007/978-3-030-58309-5_16.
• Gygax, G. and Schüle, M. (2020) 'A hybrid deep learning approach for forecasting air temperature', in Schilling, F.-P. and Stadelmann, T. (eds) Artificial Neural Networks in Pattern Recognition. Cham: Springer, pp. 235–246. doi: 10.1007/978-3-030-58309-5_19.
• Schüle, M. and Ott, T. (2018) 'Synchronization in cellular automata : the learning approach', in 2018 International Symposium on Nonlinear Theory and Its Applications (NOLTA2018), Tarragona, Spain, 2-6 September 2018.
• Ott, T. et al. (2018) 'Structural evolution in networks of coupled maps with asymmetric influence amplification', in 2018 International Symposium on Nonlinear Theory and Its Applications (NOLTA2018), Tarragona, Spain, 2-6 September 2018, pp. 546–549.
• Ott, T. et al. (2016) 'Clustered multidimensional scaling with Rulkov neurons', in 2016 International Symposium on Nonlinear Theory and Its Applications. IEICE, pp. 389–392. doi: 10.21256/zhaw-3532.
• Schüle, M., Ott, T. and Schwendner, P. (2016) 'Forecasting correlation structures', in Proceedings of the 2016 international symposium on nonlinear theory and its applications. IEICE.

Weitere Publikationen

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

Mündliche Konferenzbeiträge und Abstracts

• Schwendner, P., Schüle, M. and Hillebrand, M. (2019) 'Correlation influence networks for sentiment analysis in European sovereign bonds', in Financial Revolution - Sentiment Analysis, AI and Machine Learning, London, United Kingdom, 25-26 June 2019.
• Schwendner, P., Schüle, M. and Hillebrand, M. (2018) 'Correlation influence networks for sentiment analysis in European sovereign bonds', in Financial Revolution - Sentiment Analysis, AI and Machine Learning, Zürich, Switzerland, 30 October 2018.
• Schwendner, P. et al. (2018) 'Sentiment in European sovereign bonds', in 3rd European COST Conference on Mathematics for Industry in Switzerland, Winterthur, 6 September 2018. Available at: https://www.zhaw.ch/storage/engineering/institute-zentren/iamp/sp_acss/Schwendner_20180906.pdf.
• Schüle, M., Ott, T. and Schwendner, P. (2018) 'Influence networks in financial markets : forecast scenarios', in NDES 2018, 26th Nonlinear Dynamics of Electronic Systems Conference, Acireale, Italy, June, 11-13 2018.
• Ott, T. et al. (2018) 'A dynamic network approach for the analysis of pathogen transmission chains', in The 26th Nonlinear Dynamics of Electronic Systems Conference, (NDES 2018), Acireale, 11-13 June 2018.
• Schwendner, P., Schüle, M. and Hillebrand, M. (2017) 'Sovereign bond network dynamics', in Mathfinance Conference, Frankfurt, Germany, 20-21 April 2017.
• Schwendner, P., Schüle, M. and Hillebrand, M. (2017) 'Network analytics of sovereign bond dynamics', in Frankfurt Summit on Network Analysis, Frankfurt, Germany, 26 October 2017.
• Schüle, M., Ott, T. and Schwendner, P. (2017) 'Forecasting correlation structures', in NDES 2017, 25th Nonlinear Dynamics of Electronic Systems Conference, Zernez, 5-7 June 2017. Available at: https://www.ini.uzh.ch/~lorimert/NDES2017/assets/NDES2017_programme_booklet.pdf.
• Hillebrand, M. et al. (2016) 'European government bond dynamics and stability policies', in ADEMU Workshop on Risk-Sharing Mechanisms for the European Union, Fiesole, Italy, 20-21 May 2016.
• Schüle, M. and Schwendner, P. (2016) 'European government bond dynamics and stability policies : taming contagion risks', in 9th Financial Risks International Forum, Paris, France, 21 March 2016.
• Schwendner, P., Schüle, M. and Hillebrand, M. (2015) 'European government bond dynamics and stability policies : taming contagion risks', in Financial Risk and Network Theory, Cambridge, United Kingdom, 9 September 2015.

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