Benedikt Schmitz

Benedikt Schmitz is a PostDoc researcher at the Technical University of Darmstadt, working at the Institute of Nuclear Physics (IKP) and the Theory of Electromagnetic Fields (TEMF). His research spans multiple domains of physics including superconductivity, laser-plasma interactions, and AI-supported modeling of complex physical phenomena.

• PhD in Physics from Technical University of Darmstadt (2023)
• Master's research at Helmholtz-Zentrum Berlin (2016-2018)

Dr. Schmitz's research focuses on superconductivity, particularly magnetic field interactions with superconductors, and laser-plasma physics for particle acceleration. His work on radiochromic film dosimetry led to pyRES, an open-source evaluation tool. He pioneered AI applications in physics research, developing surrogate models using deep learning for neutron yield prediction and liquid target experiments. His research bridges traditional physics with modern computational approaches, demonstrating how machine learning can transition from research subject to research tool.

His publication record shows a clear evolution from superconductivity research toward laser-plasma physics and AI modeling. Early works focused on SRF cavity diagnostics, while recent publications center on laser-driven neutron sources and deep learning applications. This progression reflects his doctoral work and growing expertise in computational physics. His articles demonstrate interdisciplinary approaches combining plasma physics, nuclear engineering, and machine learning to solve complex problems in particle acceleration and detection.

• First prize at Medtech:Hack with BIOSCAN at CERN (April 2018)

Dr. Schmitz has led multiple research projects including SRF Magnetometry during his Master's work, Neutron Prediction and TNSA Liquid Leaf for his PhD, and ongoing development of pyRES. His BIOSCAN detector project resulted in a patent and demonstrates his ability to translate physics concepts into medical applications. He has developed software tools like LabTab for electronic lab journals and maintains active GitHub repositories for his research code. His projects consistently combine experimental work with computational modeling and increasingly incorporate machine learning approaches.

His research is conducted within collaborative teams including the TEMF group at TU Darmstadt under Prof. Boine-Frankenheim for his doctoral work, and previously with Prof. Jens Knobloch's group at Helmholtz-Zentrum Berlin. His work spans multiple laboratories and computational environments, utilizing particle-in-cell simulations, Monte Carlo methods, and deep learning frameworks to advance understanding in his fields of interest.