Simon Hirländer
Vice Director, IDA Lab · Head of Smart Analytics & Reinforcement Learning
Department of Artificial Intelligence and Human Interfaces · University of Salzburg
Theoretical Physicist and Decision Scientist bridging rigorous mathematical physics (Hamiltonian mechanics, symplectic geometry, potential theory) with modern Deep Reinforcement Learning. In 2018/19 I designed and carried out the first deep reinforcement learning experiment at CERN, on the Low Energy Ion Ring. In 2020 I brought reinforcement learning to the University of Salzburg: what started as a single seminar topic is now a regular part of the curriculum, with a Reinforcement Learning lecture and exercise in the BSc Artificial Intelligence and advanced RL courses in the MSc Data Science. Today, my research team develops safe, uncertainty-aware, and physics-informed AI systems for critical physical infrastructures and industrial processes.
- Role
- Vice Director, IDA Lab · Head of Smart Analytics and Reinforcement Learning
- Funding
- €1.89M across 20 funded projects, principal investigator on 19
- Teaching
- Introduced RL at Salzburg (2020) · now in the BSc AI curriculum (VO/UE 536.111/112) · MSc Data Science · courses
- Supervision
- 10 doctoral, 7 master and 11 bachelor theses
- Collaboration
- CERN · GSI/FAIR · DLR · COPA-DATA · Salzburg Research
- Recent
- Invited talk, NeurIPS 2026 workshop, Paris · four IPAC’26 papers · EPJ Research Infrastructures, Critical Care
Core Research Pillars
Integrating physical invariants and control guarantees into modern decision-making architectures.
Recent News & Highlights
Invited Talk at NeurIPS 2026 Workshop (Paris)
Invited talk on "Koopman Stabilised Symplectic World Models for Safe Offline-to-Online RL in Particle Accelerators" at the NeurIPS 2026 Workshop RL for Experimental Sciences: Bridging the Simulation-to-Reality Gap in Paris.
Podcast: S2 #17 | Reinforcement Learning in Business
Featured episode on DIH West's EASY cheesy DIGITAL exploring decision-making under uncertainty, agentic AI, and business transformation through Reinforcement Learning.
Four IPAC'26 Papers Accepted
Contributions on Koopman-stabilised world models, Causal GP-MPC, delayed-consequence RL beyond greedy optimization, and SIS18 injection optimization accepted for presentation.
KI-MINTFIT Doctoral Program Launches (€1.08M)
Launch of the cooperative doctoral program funded by the Austrian BMBWF in collaboration with PH Salzburg, advancing AI-driven STEM subject instruction and student models.
Real-World Impact & Results
Demonstrated breakthroughs across extreme, high-dimensional experimental environments.
Automated tuning at the GSI/FAIR SIS18 heavy-ion synchrotron (Geoff framework, EPJ Research Infrastructures 2026); my team contributed the data-driven Gaussian Process MPC.
A Deep Q-Network agent learned, directly on the machine, to steer the beam at injection into the Low Energy Ion Ring (LEIR). Presented at the 2nd ICFA ML Workshop, PSI, Feb 2019 (slides); followed by the AWAKE and LINAC4 experiments (PRAB 23, 124801, 2020).
Trained Stable Koopman State-Space Models on 58M+ operational industrial time-series points with Danieli Automation, achieving high-accuracy torque and tension prediction.
Institutional Partners & Collaborators
Interested in Research Collaboration or Theses?
We regularly supervise motivated MSc and PhD students and collaborate with industrial partners and research labs on reinforcement learning and autonomous systems.