Forschungsschwerpunkt Biolaborautomatisierung

• Management großer Datenmengen im Laborkontext (FAIR data)
• Integration von Hardware- und Software-Komponenten der Automatisierung
• Machine Learning auf multimodalen Labordaten (Bild- und omics-Daten)
• Robotergestützter Umgang mit Flüssigkeiten

Berufserfahrung

• Seit 12/2020: Wissenschaftlicher Mitarbeiter und Doktorand am Munich Institute of Robotics and Machine Intelligence (MIRMI) der TU München und der Munich School for Data Science (MUDS) bei Prof. Dr.-Ing. Sami Haddadin
• 01/2017 - 11/2020: Senior Quantitative Business Modeler im Horváth & Partners Steering Lab

Ausbildung

• 08/2012 - 10/2015: M. Sc. in Physik an der Georg-August-Universität Göttingen, an der Universität Stockholm und am Max-Planck-Institut für Dynamik und Selbstorganisation, Göttingen
• 10/2009 - 07/2012: B. Sc. in Physik an der Georg-August-Universität Göttingen und am Max-Planck-Institut für Dynamik und Selbstorganisation, Göttingen

Veröffentlichungen

• H. Zwirnmann, D. Knobbe, U. Culha and S. Haddadin, "Towards Flexible Biolaboratory Automation: Container Taxonomy-Based, 3D-Printed Gripper Fingers," accepted for publication at the 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Detroit, USA, 2023.
• M. Eckhoff, H. Zwirnmann, A. Swikir and S. Haddadin, "Towards Connecting Control to Perception: High-Performance Whole-Body Collision Avoidance using Control Compatible Obstacles," accepted for publication at the 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Detroit, USA, 2023.
• H. Zwirnmann, D. Knobbe and S. Haddadin, "Towards Automated End-to-End Control for Holotomography Microscopy: Workflow Design and Data Management," 22nd World Congress of the International Federation of Automatic Control (IFAC), Yokohama, Japan, 2023.
• D. Knobbe, H. Zwirnmann, M. Eckhoff and S. Haddadin, "Core Processes in Intelligent Robotic Lab Assistants: Flexible Liquid Handling," 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, 2022, pp. 2335-2342, doi: 10.1109/IROS47612.2022.9981636.
• C. Moldenhauer and H. Zwirnmann, "Basket Analysis in Practice: Mathematical Models and Applications in Offline Retail," in Performance Management in Retail and the Consumer Goods Industry, M. Buttkus and R. Eberenz, Ed. Cham (Germany): Springer, 2019, pp. 369–384, doi: 10.1007/978-3-030-12730-5_24.
• A. Schlemmer, H. Zwirnmann, M. Zabel, U. Parlitz and S. Luther, "Evaluation of machine learning methods for the long-term prediction of cardiac diseases," 2014 8th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO), Trento, Italy, 2014, pp. 157-158, doi: 10.1109/ESGCO.2014.6847567.

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