Prof. Dr. Thomas Ott

ZHAW Life Sciences und Facility Management
Institut für Computational Life Sciences
Schloss
8820 Wädenswil

Arbeit an der ZHAW

Tätigkeit

Institutsleitung Institut für Computational Life Sciences

Arbeits- und Forschungsschwerpunkte

Bio-inspired Computing, Applied Neuroinformatics (Pattern Recognition, Machine Learning), Modelling of Complex Systems Forecast methodologies for traffic and logistics Digital Health Assistance Systems

Lehrtätigkeit

Systeme und Modelle der Physik (BSc ADLS)
Optimisation Methods and Bio-inspired Algorithms (MSc Life Sciences ACLS)

Netzwerk

Mitglied in Netzwerken

ORCID digital identifier

ORCID ID: 0000-0003-1635-7816

Social Media

Projekte

Publikationen

Beiträge in wissenschaftlicher Zeitschrift, peer-reviewed

Wróbel, A., Sandamirskaya, Y., & Ott, T. (2025). Implementing a neuromorphic classifier for NIR spectra analysis of fruits. Nonlinear Theory and Its Applications, 16(2), 222–232. https://doi.org/10.1587/nolta.16.222
Glüge, S., Balabanov, S., Koelzer, V. H., & Ott, T. (2023). Evaluation of deep learning training strategies for the classification of bone marrow cell images. Computer Methods and Programs in Biomedicine, 243, 107924. https://doi.org/10.1016/j.cmpb.2023.107924
Epper, P., Glüge, S., Vidondo, B., Wróbel, A., Ott, T., Sieme, H., Kaeser, R., & Burger, D. (2023). Increase of body temperature immediately after ovulation in mares. Journal of Equine Veterinary Science, 127(104565). https://doi.org/10.1016/j.jevs.2023.104565
Garcia, V., Horn, C., & Ott, T. (2022). The sciences of data – moving towards a comprehensive systems perspective. Archives of Data Science, Series A, 8(2). https://doi.org/10.5445/IR/1000150241
Müller, A., Glüge, S., Vidondo, B., Wróbel, A., Ott, T., Sieme, H., & Burger, D. (2022). Increase of skin temperature prior to parturition in mares. Theriogenology, 190, 46–51. https://doi.org/10.1016/j.theriogenology.2022.07.007
Uwate, Y., Nishio, Y., & Ott, T. (2021). Synchronization of chaotic circuits with stochastically-coupled network topology. International Journal of Bifurcation and Chaos, 31(1), 2150015. https://doi.org/10.1142/S0218127421500152
Uwate, Y., Takamaru, Y., Ott, T., & Nishio, Y. (2019). Clustering using chaotic circuit networks with weighted couplings. International Journal of Bifurcation and Chaos, 29(4), 1950053. https://doi.org/10.1142/S0218127419500536
Tanner, C., Narvaez, D., Christen, M., & Ott, T. (2016). Using thesauruses as a heuristics for mapping values. Cognitive Systems Research, 40, 59–74. https://doi.org/10.1016/j.cogsys.2016.02.003
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
Christen, M., Niederberger, T., Ott, T., Aryobsei, S., & Hofstetter, R. (2015). Micro-text classification between small and big data. Nonlinear Theory and Its Applications, 6(4), 556–569. https://doi.org/10.1587/nolta.6.556
Christen, M., Ott, T., & Schwarz, D. (2013). A new measure for party coherence : applying a physics-based concept to the Swiss party system. Advances in Complex Systems, 16(6), 1350011. https://doi.org/10.1142/S0219525913500112
Savi, D., Kasser, U., & Ott, T. (2013). Depollution benchmarks for capacitors, batteries and printed wiring boards from waste electrical and electronic equipment (WEEE). Waste Management, 33(12), 2737–2743. https://doi.org/10.1016/j.wasman.2013.08.014
Landis, F., Ott, T., & Stoop, R. (2010). Hebbian self-organizing integrate-and-fire networks for data clustering. Neural Computation, 22(1), 273–288. https://doi.org/10.1162/neco.2009.12-08-926

Buchbeiträge, peer-reviewed

Ott, T., Glüge, S., Bödi, R., & Kauf, P. (2019). Economic measures of forecast accuracy for demand planning : a case-based discussion. In M. Braschler, T. Stadelmann, & K. Stockinger (Eds.), Applied data science : lessons learned for the data-driven business (pp. 371–386). Springer. https://doi.org/10.1007/978-3-030-11821-1_20
Christen, M., & Ott, T. (2013). Quantified coherence of moral beliefs as predictive factor for moral agency. In B. Musschenga & A. van Harskamp (Eds.), What makes us moral? : on the capacities and conditions for being moral (pp. 73–96). Springer. https://doi.org/10.1007/978-94-007-6343-2_5

Schriftliche Konferenzbeiträge, peer-reviewed

Wróbel, A., Sandamirskaya, Y., & Ott, T. (2023, September). A spiking neural network for classifying NIR spectra of fruits. International Symposium on Nonlinear Theory and Its Applications (NOLTA), Catania, Italy, 26-29 September 2023. https://doi.org/10.21256/zhaw-30070
Gygax, G., Ratnaweera, N., Tischhauser, W., Smits, T. H. M., Laube, P., & Ott, T. (2023). An oracle for the optimization of underconstrained compositions of neural networks : the tick hazard use case [Conference paper]. In J. J. Schneider, M. S. Weyland, D. Flumini, & R. M. Füchslin (Eds.), Artificial Life and Evolutionary Computation (pp. 24–31). Springer. https://doi.org/10.1007/978-3-031-23929-8_3
Füchslin, R. M., Ambühl, J., Faggian, A., Fellermann, H. M., Flumini, D., Geller, A., Hanczyc, M. M., Klinkert, A., Krütli, P., Matuttis, H.-G., Ott, T., Scheidegger, S., Schmid, G. B., Smieszek, T., Schneider, J. J., Steiner, A., & Weyland, M. S. (2023, January 22). Ethical aspects of computational modelling in science, decision support and communication. Artificial Life and Evolutionary Computation. https://doi.org/10.1007/978-3-031-23929-8_24
Vachey, G., & Ott, T. (2022). Self-organizing maps of artificial neural classifiers : a brain-like pin factory [Conference paper]. In J. J. Schneider, M. S. Weyland, D. Flumini, & R. M. Füchslin (Eds.), Artificial Life and Evolutionary Computation (pp. 163–171). Springer. https://doi.org/10.1007/978-3-031-23929-8_16
Uwate, Y., Ott, T., & Nishio, Y. (2021). Effect of stochastically coupling on frustrated triangular oscillatory network. 2021 IEEE International Symposium on Circuits and Systems (ISCAS). https://doi.org/10.1109/ISCAS51556.2021.9401462
Uwate, Y., Nishio, Y., & Ott, T. (2021). Frustrated complex networks of nonlinear circuits with stochastically coupling [Conference paper]. 2020 International SoC Design Conference (ISOCC), 181–182. https://doi.org/10.1109/ISOCC50952.2020.9333023
Gygax, G., Füchslin, R. M., & Ott, T. (2020). Self-organized division of labor in networks of forecasting models for time series with regime switches [Conference paper]. Proceedings of the NOLTA 2020 Conference, 278–281.
Miniussi, M., Ott, T., & Fellermann, H. (2020). Impact of noise and network size in coupled maps with asymmetric influence amplification [Conference paper]. Proceedings of the NOLTA 2020 Conference, 282–285.
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.
Wróbel, A., Gygax, G., Schmid, A., & Ott, T. (2020). Going for 2D or 3D? : investigating various machine learning approaches for peach variety identification [Conference paper]. In F.-P. Schilling & T. Stadelmann (Eds.), Artificial Neural Networks in Pattern Recognition (pp. 257–265). Springer. https://doi.org/10.1007/978-3-030-58309-5_21
Füchslin, R. M., Schneider, J. J., Ott, T., & Walker, R. (2019). Simplified modeling of the evolution of skills in a spatially resolved environment [Conference paper]. In H. Fellermann, J. Bacardit, A. Goñi-Moreno, & R. M. Füchslin (Eds.), Proceedings of the Artificial Life Conference 2019 (pp. 324–330). Massachusetts Institute of Technology. https://doi.org/10.1162/isal_a_00182
Uwate, Y., Ott, T., & Nishio, Y. (2018). Producing complex networks using coupled oscillatory circuits with evolutionary connections [Conference paper]. IEEE International Symposium on Circuits and Systems (ISCAS), 1–5. https://doi.org/10.1109/ISCAS.2018.8351665
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.
Glüge, S., Böck, R., & Ott, T. (2017). Emotion recognition from speech using representation learning in extreme learning machines [Conference paper]. In C. Sabourin, J. Julian Merelo, U.-M. O’Reilly, K. Madani, & K. Warwick (Eds.), Proceedings of the 9th International Joint Conference on Computational Intelligence (pp. 179–185). SciTePress. https://doi.org/10.5220/0006485401790185
Schüle, M., Ott, T., & Schwendner, P. (2016). Forecasting correlation structures. Proceedings of the 2016 International Symposium on Nonlinear Theory and Its Applications.
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
Nef, A., Glüge, S., Ott, T., & Kauf, P. (2014). Causality detection in complex time dependent systems examplified in financial time series [Conference paper]. Proceedings of the 2014 International Symposium on Nonlinear Theory and Its Applications (NOLTA2014), 176–179. http://www.ieice.org/nolta/symposium/archive/2014/nolta14fullvol.pdf
Eggel, T., Christen, M., & Ott, T. (2014). Generating low-dimensional denoised representations of nonlinear data with superparamagnetic agents [Conference paper]. Proceedings of the 2014 International Symposium on Nonlinear Theory and Its Applications (NOLTA2014), 180–183. https://doi.org/10.21256/zhaw-3565
Glüge, S., Pomati, F., Albert, C., Kauf, P., & Ott, T. (2014). The challenge of clustering flow cytometry data from phytoplankton in Lakes [Conference paper]. In V. M. Mladenov & P. C. Ivanov (eds.), Nonlinear Dynamics of Electronic Systems 22nd International Conference, NDES 2014, Albena, Bulgaria, July 4-6, 2014. Proceedings (pp. 379–386). Springer. https://doi.org/10.1007/978-3-319-08672-9_45
Bruckmann, D., Jackson, C., Ott, T., & Weidmann, U. (2014). Improving the forecast of freight transport demand using machine learning and time series analysis. 14th Swiss Transport Research Conference, Ascona, 14-16 May 2014.
Uwate, Y., Ott, T., & Nishio, Y. (2013). Clustering phenomena in coupled chaotic circuits with different coupling strength. European Conference on Circuit Theory and Design (ECCTD), Dresden, Deutschland, 8-12 September 2013. https://doi.org/10.1109/ECCTD.2013.6662220
Müller, C., Grabherr, P. M., Ott, T., Brombach, C., & Hauck, R. (2013, October). A user-friendly tool for assessing sustainability of menus in large-scale catering. Resource efficiency, governance and lifestyles : meeting report.
Takamaru, Y., Uwate, Y., Ott, T., & Nishio, Y. (2012). Clustering phenomena of coupled chaotic circuits for large scale networks [Conference paper]. Nonlinear Dynamics of Electronic Systems, Proceedings of NDES 2012, 70–73. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6293766
Niederberger, T., Stoop, N., Christen, M., & Ott, T. (2012). Hebbian principal component clustering for information retrieval on a crowdsourcing platform [Conference paper]. Proceedings of NDES 2012, 249–252. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6293769
Niederberger, T., Merlo, O., & Ott, T. (2011). Predator-prey dynamics in Hopfield-type networks [Conference paper]. Proceedings of the 2011 International Symposium on Nonlinear Theory and Its Applications (NOLTA2011), 541–544. http://www.ieice.org/proceedings/NOLTA2011/nolta11fullvol.pdf
Mürset, U., & Ott, T. (2010). Data clustering based on Hebbian learning in inhomogeneous coupled map lattices. Proceedings of NDES.
Christen, M., Starostina, T., Schwarz, D., & Ott, T. (2009). A spin-based measure of the coherence of belief-systems : theory and application. Proceedings Od NDES′09.
Mürset, U., & Ott, T. (2009). Semi-supervised classifiaction with single-layer isingtrons. Proceedings of NDES′09.
Jasa, T., Stoop, R., Ott, T., & Lanz, T. (2008). Sequential superparamagnetic clustering as a predictor of visual fixations [Conference paper]. Proceedings of NOLTA (Nonlinear Theory and Applications), Budapest, Hungary, September 7-10, 2008, 120–123. http://www.ieice.org/proceedings/NOLTA2008/nolta08fullvol.pdf
Schüle, M., Stoop, R., & Ott, T. (2008). Global dynamics of finite cellular automata [Conference paper]. Artificial Neural Networks - ICANN 2008 : Proceedings Part I, 71–78. https://doi.org/10.1007/978-3-540-87536-9_8

Weitere Publikationen

Füchslin, R. M., Krütli, P., Ott, T., Scheidegger, S., Schneider, J. J., Seric, M., Smieszek, T., & Weyland, M. S. (2022). Minimal models for spatially resolved population dynamics : applications to coexistence in multi – trait models [Conference paper]. ALIFE 2022: The 2022 Conference on Artificial Life, 75. https://doi.org/10.1162/isal_a_00504
Kauf, P., & Ott, T. (2017). Smart together : lernfähige Prognose-Algorithmen unterstützen Menschen. Logistics Innovation, 2017(1), 32–35.
Dorndorf, U., Kauf, P., & Ott, T. (2015). Bessere Absatzprognosen : eine Frage der Faktoren. Logistics Innovation, 2015(2), 8–11.
Burkhard, M., Ott, T., Stoop, N., Kauf, P., Jackson, C. E., Frei, S., Ruesch, M., Hegi, P., Moreni, G., & Koy, T. (2014). Stauprognoseverfahren und -systeme : Forschungsprojekt ASTRA 2011/010. Bundesamt für Strassen.
Köhler, S., & Ott, T. (2013). Einflussfaktoren auf das Patientenaufkommen in Notfallstationen. Transfer, 2013(1), 7. https://doi.org/10.21256/zhaw-4415
Takamaru, Y., Uwate, Y., Ott, T., & Nishio, Y. (2013). Dependence of clustering patterns on density of chaotic circuits in networks. Journal of Signal Processing, 17(4), 103–106. https://doi.org/10.2299/jsp.17.103
Koller, C., Kellenberger, R., Ott, T., & Meyer, T. (2013). PV-Tracking-System auf dem Skilift in Tenna - eine erste Auswertung : Annual Report 2012. In Programm Photovoltaik Ausgabe 2013 : Überblicksbericht, Liste der Projekte : Jahresberichte der Beauftragen 2012 (pp. 251–256). Bundesamt für Energie. https://netenergy.ch/fileadmin/webmaster/dokumente/Jahresberichte/PVJB2013_4th.pdf
Ott, T., & Krüsi, B. (2012). Zukünftige Bestimmungsschlüssel denken mit. Transfer, 2012(3), 6. https://doi.org/10.21256/zhaw-4410
Krüsi, B., Lustenberger, P. I., Hepenstrick, D., & Ott, T. (2012). iGräser - das innovative und benutzerfreundliche mobile Gräserbestimmungs-Tool fürs iPhone. Ingenieurbiologie, 2012(3), 61–71. https://doi.org/10.21256/zhaw-2033
Christen, M., Ott, T., & Schwarz, D. (2011). Spaltpilz in den Parteien : mit Physik die Politik erklären. Neue Zürcher Zeitung.

Mündliche Konferenzbeiträge und Abstracts

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
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.
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. (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
Ott, T., & Füchslin, R. M. (2017, June 5). The future’s back : first generic models of self-defeating road traffic forecasting. NDES 2017, 25th Nonlinear Dynamics of Electronic Systems Conference, Zernez, 5-7 June 2017.
Ott, T. (2016). (Machine × Human Learning)^n – Partners Learning Together : Keynote. CeBIT 2016, Hannover, Deutschland, 14.-18. März 2016.
Kauf, P., & Ott, T. (2016). Humans and algorithms : creation and measurement of economic value in demand forecasting. 3rd Swiss Conference on Data Science, Winterthur, 16. September 2016. http://www.zhaw.ch/storage/hochschule/institute-zentren/datalab/SDS/2016/Slides/kauf.pdf
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.
Ott, T. (2015). Big Data und Produktionssteuerung in der Lebensmittel-Industrie. Wädenswiler Lebensmitteltagung: Lebensmittel 4.0, Wädenswil, 19. November 2015.
Ott, T. (2015). A spin physics-based framework for hybrid recommendation systems. NDES 2015, 23th Nonlinear Dynamics of Electronic Systems Conference, Como, Italy, 2015.
Ott, T. (2014). Stauentwicklungen : Daten- und modellgestützte Erkenntnisse zur Verkehrsflussprognose und Verkehrsflussoptimierung. 1. Nationaler Anti-Stautag Viasuisse, Luzern, 19. Mai 2014.
Ott, T. (2014). Complex forecasting. Rüdlinger Tagung 2014, Rüdlingen, 9.-10. September 2014.
Ott, T., Christen, M., Niederberger, T., Aebersold, R., Suleiman, A., & Hofstetter, R. (2013). A semi-supervised learning system for micro-text classification. NDES 2013, 21st Nonlinear Dynamics of Electronic Systems Conference, Bari, Italy, 2013.
Niederberger, T., Ott, T., Albert, C., & Pomati, F. (2013). A sequential classification concept using flawed training data : an application to flow cytometry. NDES 2013, 21st Nonlinear Dynamics of Electronic Systems Conference, Bari, Italy, 2013.
Grabherr, P. M., Ott, T., Brombach, C., Hauck, R., & Müller, C. (2013, March). Benutzerfreundliches Tool zur Nachhaltigkeitsbeurteilung von Menüs in der Gemeinschaftsgastronomie. 50. Wissenschaftlicher Kongress der DGE, Bonn, Deutschland, 20.-22. März 2013.

Publikationen vor Tätigkeit an der ZHAW

• T. Ott and R. Stoop; Benefits and Pitfalls of Belief Propagation-mediated Superparamagnetic Clustering. Physical Review E 74 (4): 042103, 2006. • M. Christen, A. Nicol, K. Kendrick, T. Ott and R. Stoop; Odour Encoding in Olfactory Neuronal Networks Beyond Synchronisation. Neuroreport, 17 (14): 1499-1503, 2006. • M. Christen, A. Kohn, T. Ott and R. Stoop; Measuring Spike Pattern Reliability with the Lempel-Ziv Distance. Journal of Neuroscience Methods, 156: 342-350, 2006. • T. Ott and R. Stoop; The Neurodynamics of Belief Propagation on Binary Markov Random Fields. Advances in Neural Information Processing Systems NIPS ’06, 2006. • R. Stoop and T. Ott; Towards a Quantitative Theory of Biocomputation. Proc. of NOLTA ’06 Bologna, 2006. • N. Stoop, T. Ott and R. Stoop; Loopy Belief Propagation: Benefits and Pitfalls on Ising-like Systems. Proc. of NOLTA ’06 Bologna, 2006. • T. Ott, A. Kern and R. Stoop; Faster Spike Sorting with Belief Propagation. Proc. of NOLTA ’06 Bologna, 2006.
• A. Kern, T. Ott and R. Stoop; Acoustic Source Separation by Atomic Signal Decomposition. Proceedings of NOLTA ’06 Bologna, 2006. • A. Kern, T. Ott and R. Stoop; Source Separation by Atomic Signal Decomposition. Proc. of NDES’06, p. 77-80, 2006. • T. Ott, M. Christen and R. Stoop; An Unbiased Clustering Algorithm Based on Self-organisation processes in Spiking Neural Networks. Proc. of NDES’06, p. 143-146, 2006. • Y. Uwate, T. Ott, R. Stoop and Y. Nishio; Performance of Feedforward Neural Network with External Influence Function for Back Propagation Learning. RISP InternationalWorkshop on Nonlinear Circuits and Signal Processing (NCSP’06), 2006. • T. Ott, A. Kern, W.-H. Steeb and R. Stoop; Sequential Clustering: Tracking Down the Most Natural Clusters. Journal of Statistical Mechanics: theory and experiment: P11014, 2005.
• M. Christen, T. Ott, A. Kern, N. Stoop and R. Stoop; Periodic Economic Cycles: The Effect of Evolution and Control. Journal of Statistical Mechanics: theory and experiment: P11013, 2005. • T. Ott, M. Frey, R. Stoop; Criticality and Computation in Random Threshold Networks with Noise. Proc. of NOLTA 2005 Bruges, 2005. • T. Ott, J. Dauwels and R. Stoop; Sequential Clustering by Loopy Belief Propagation. Proc. of ECCTD 2005 Cork, 2005. • T. Ott, A. Kern, A. Schuffenhauer, M. Popov, P. Acklin, E. Jacoby and R. Stoop; Sequential Superparamagnetic Clustering for Unbiased Classification of High-dimensional Chemical Data. Journal of Chemical Information and Computer Sciences, 44 (4): 1358-1364, 2004.
• J.-J. van der Vyver, M. Christen, N. Stoop, T. Ott, W.-H. Steeb and R. Stoop; Towards Genuine Machine Autonomy. Robotics and Autonomous Systems, 46 (3): 151-157, 2004. • T. Ott and R. Stoop; Human-like Perception Using Sequential Superparamagnetic Clustering. Proceedings of NOLTA’04 Fukuoka, 2004. • T. Ott, W.-H. Steeb and R. Stoop; The Stochastic Network Approach to Clustering. Proc. of NOLTA’04 Fukuoka, 2004. • M. Christen, T. Ott and R. Stoop; Spike Train Clustering Using a Lempel-Ziv Complexity-based Distance Measure. Proc. of NOLTA’04 Fukuoka, 2004.

Interessenbindungen

Aktuelle Interessenbindungen

Prognosix AG: Member of Board of Directors
Mitglied Verwaltungsrat, Zürich
03 / 2017 - heute
Life Sciences Zurich Business Network: Vorstandsmitglied LSZBN
Vertretung Departement LSFM im Vorstand des Life Sciences Zurich Busniness Network, Zürich
06 / 2024 - heute
Biotechnet Switzerland: Member of executive board of Biotechnet
Member of the executive board of Biotechnet. Vertretung des Instituts ICLS im Netzwerk, Muttenz
01 / 2025 - heute

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