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Project Team

NoctuaNet2 / Project Team

Core team

Andreas Brandstätter
© TU Wien, Andreas Brandstätter

Dr. Andreas Brandstätter

Expertise on coordination of autonomous drone swarms; low-level real-time behavior with multiple publications; PhD in Computer Engineering: Coordination and Control of Robotic Multi-agent Systems in Confined Environments.

Joel Klimont

DI Joel Klimont

Focus on GNSS-denied navigation. Won the Aerial Competition at ECER and GCER in 2018 and 2019, for autonomous landing & package delivery. Best Young Team award at ENRICH 2023, 2nd at IMAV 2024. Quarterfinals at A2RL autonomous drone racing in Abu Dhabi 2025, won multidrone racing silver group in 2026, and Best 3D Mapping at ENRICH 2025.

Jaroslav Klapalek

DI Jaroslav Klapalek

Research focus on autonomous driving, especially trajectory planning in dynamic environments. Background in Robotics and Cybernetics, with expertise in various industrial and research projects, e.g., European project Arrowhead Tools for Engineering of Digitalisation Solutions.

Felix Resch
© TU Wien, Andreas Brandstätter

DI Felix Resch

Focusing on event-based vision, neuromorphic hardware, and bio-inspired neural networks for autonomous agents. Background in computer engineering with a focus on reliable real-time systems and hardware design. Won 1st place at CDC 2024 and 4th at ICRA 2025 for autonomous car racing.

Scientific Advisors

Ezio Bartocci

Univ.Prof. Ezio Bartocci

Full Professor for Formal Methods in Cyber-Physical Systems Engineering at the Faculty of Computer Science TU Wien, and leading the Trustworthy Cyber-Physical Systems (TrustCPS) Group of the Cyber-Physical System Research Unit.

Radu Grosu

Univ.Prof. Radu Grosu

Head of Research Unit Cyber-Physical Systems; Research in Supervised and
Reinforcement Learning, Explainable AI, Biophysical Neural Models, Control of Cyber-Physical and Biological Systems, Model Checking, Abstract Interpretation, Logic and
Automata Theory, Control Theory

Monika Farsang

DI Monika Farsang

Researcher on Reinforcement & Supervised Learning Neural Circuit Policies; Developing Efficient Bio-inspired Machine Learning Models; Controlling Agents via Bio-inspired Recurrent Neural Networks; Interpretability of Artificial Neural Networks

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