Research

My research focuses on Reinforcement Learning (RL) and its application to complex, real-world systems. I lead the Smart Analytics & Reinforcement Learning (SARL) team, bridging the gap between theoretical advancements and industrial applications.


Core Research Areas

Sample-Efficient RL
Learning from limited data for physical systems
Safe RL
Constrained optimization for critical infrastructure
Industrial Control
Optimization of manufacturing & energy systems
Accelerator Physics
AI for particle accelerators (CERN, DESY, etc.)

Current Projects

FOCUS 2025 - 2028

Forecasting and optimization under constraints and uncertainty for sustainable industrial energy systems

Principal Investigator. Contract research project in collaboration with Ing. Punzenberger COPA-DATA GmbH. Developing advanced RL and optimization methods for managing industrial energy systems under uncertainty.

Principal Investigator Energy Industrial AI
INSPIRE 2026 - 2029

Intelligent Novel Support for Personalized Instruction and Robust Evaluation in STEM Lessons

Principal Investigator (PLUS partner). Application-oriented basic research project funded by Land Salzburg (WISS2030) in collaboration with PH Salzburg. Focusing on AI-driven personalized learning and evaluation in primary education.

Principal Investigator Education AI for Good
SCOPE 2025 - 2026

Smart Component Process Evaluation

Co-Lead. Research project focusing on smart component evaluation and process optimization.

Industrial AI Process Evaluation
Gamma Factory

The Gamma Factory is an ambitious CERN initiative to create a "super light source" by colliding laser pulses with partially stripped ions in the LHC. My involvement focuses on the accelerator physics challenges of maintaining stable ion beams and optimizing the collision luminosity, leveraging the same advanced control techniques used for the standard LHC runs.

CERN Accelerator Physics High-Energy Physics
Wireless Networks & UAVs

Researching Multi-Agent Reinforcement Learning (MARL) applications in 5G/6G wireless communication. Focusing on optimizing UAV-assisted networks to minimize information age (AoI) and energy consumption, enabling autonomous, self-optimizing aerial network swarms.

Multi-Agent Systems UAVs Information Freshness (AoI)
RL-PD 2025 - 2026

Regelungsoptimierung von Piezoantrieben

Optimization of piezoelectric device drives.

Medical Control Theory Piezo
DS STIWA II 2025 - 2026

Data Science STIWA II

Research project with STIWA Group. Focusing on assembly line optimization using Hierarchical Reinforcement Learning to manage complex, multi-stage decisions in high-speed manufacturing.

Manufacturing Hierarchical RL Optimization
NauROM Completed

Nautic Robotics Occupation Maps

Research cooperation with EvoLogics GmbH and Paris Lodron University Salzburg (PLUS) / Dept. of AI & Human Interfaces. Transferring control theory expertise to autonomous maritime systems, leveraging similarities between fluid dynamics and beam physics.

Funding: EvoLogics GmbH (contract research) · Duration: Oct 2024 – Mar 2025 · Team: S. Hirländer (PI), C. Borgelt, S. Dabarera

Robotics Maritime Control Theory Industry

Doctoral Research

PhD Thesis Completed

Exact Solutions of Indirect Transverse Field Effects in Elongated Structures with Applications to CERN LHC and PS

My doctoral research focused on the derivation of exact solutions for indirect transverse field effects in elongated structures, with direct applications to the CERN Large Hadron Collider (LHC) and Proton Synchrotron (PS). This work addressed the critical challenge of Indirect Space Charge Driven (ISCD) effects, which serve as a fundamental limit to beam stability and intensity.

I developed a novel theoretical framework utilizing complex Green functions to accurately model ISCD tune-shifts, providing the first comprehensive explanation for intensity-dependent phenomena observed in the PS Multi-Turn Extraction. Key contributions include the derivation of closed-form solutions for magnetic interactions in accelerator components and the development of new operators for tune-shift estimation. These models provide essential insights for mitigating beam instabilities in current operations and are scalable for future projects like the High-Luminosity LHC (HL-LHC).

Accelerator Physics CERN Green Functions LHC

Completed Projects

DeepREL

Completed

Deep Reinforcement Learning for Large and Complex Systems. International collaboration with University of Malta and CERN. Solving complex control problems for the LHC.

CERN LHC

Danieli X – PLUS

Mar 2025 - Aug 2025

Research collaboration with Danieli Automation focusing on applied research in steel automation.

Steel Industry

DEOP 2.5

Feb 2025 - Aug 2025

Dynamische Energieoptimierung. Applied research project with COPA-DATA GmbH focusing on dynamic energy optimization.

Energy

UNSAD

Sep 2023 - May 2024

Unsupervised Anomaly Detection in Sterilisation Devices. Research collaboration with W&H Dentalwerk Bürmoos GmbH.

Medical Anomaly Detection

AREP

Sep 2022 - May 2023

Automatische Rechnungsprüfung. Applied research project with JUIX GmbH focusing on automated invoice auditing.

FinTech

MuMa II & III

Mar 2022 - May 2022

Mustererkennung Maschinendaten. Research collaborations with Palfinger AG on pattern recognition in machine data.

Manufacturing

Redlink

May 2024 - July 2024

Research co-operation with Redlink GmbH exploring the intersection of Neuro-Symbolic AI and Knowledge Graphs.

Neuro-Symbolic AI Knowledge Graphs

DS STIWA

Sep 2024 - Feb 2025

Data Science STIWA. Project Researcher (Lead: Trutschnig).

Manufacturing

DEOP 2.0

Oct 2024 - Jan 2025

Dynamische Energieoptimierung 2.0. Project Lead.

Energy

LLMs for Enterprise

May 2024 - July 2024

Project Lead. Exploring Large Language Models for enterprise applications.

LLM Enterprise AI

Collaborations

I actively collaborate with leading research institutions and industry partners:

CERN DESY KIT SLAC GSI DLR COPA-DATA Palfinger Danieli W&H STIWA Group JUIX Redlink University of Oxford University of Malta Stanford University LANL AI Austria
Stylolites: Mathematical Modeling

Stylolites are rough surfaces formed by pressure solution in rocks. This research applied statistical physics and non-linear dynamics to model their formation and morphology. By treating rock deformation as a complex system, we derived scaling laws that describe the roughness of these geological features, drawing parallels to crack propagation and growth processes in physics.

Geophysics Pattern Formation Non-linear Dynamics