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• Bioinformatics
• High Performance Computing
• Intelligent Scheduling
• +4 more

Peter Kulczycki is a researcher at the Research Center Hagenberg Bioinformatics, University of Applied Sciences Hagenberg. He serves as a Researcher specializing in bioinformatics and high-performance computing with expertise in developing computational solutions for biological research challenges. His research interests span multiple domains: Bioinformatics and computational biology High Performance Computing and parallel processing Intelligent scheduling algorithms Resource management systems DNA analysis and primer design Web-based bioinformatics education Dr. Kulczycki's work focuses on bridging computational science and biological research through efficient algorithms and systems for processing complex biological data. He has made significant contributions to scheduling systems that optimize heterogeneous computing resources for bioinformatics applications, with research progression from foundational bioinformatics resources to sophisticated scheduling systems that predict job execution times. His publication record shows consistent focus on computational efficiency in bioinformatics through innovative scheduling approaches, with notable work on heterogeneous multiplatform hardware, DNA primer calculation using GPGPUs, and web-based bioinformatics education frameworks. Dr. Kulczycki served as Principal Investigator for the Bioinformatics Resource Facility Hagenberg project (2009-2012) under the COIN Cooperation & Innovation program. He has been active in academic discourse, delivering invited talks on Evolution and Genetics and the Birth of Life in 2007. His research collaborations include G. Lirk, H. Brandstätter-Müller, B. Parsapour, and A. Hölzlwimmer on various bioinformatics projects.

• Structural Biology
• Computational Biology
• Super-Resolution Microscopy
• +4 more

Jonas Ries is a Professor at the University of Vienna, affiliated with the Department of Structural Biology and Computational Biology. His research focuses on super-resolution microscopy, cryo-electron microscopy, and structural analysis of cellular components. Department: Structural Biology and Computational Biology Specialization: Nuclear pore complex architecture, MINFLUX nanoscopy, fluorescent labeling. His recent work includes 2025 projects on mitochondrial fission during apoptosis via SMLM and MINFLUX, and developing a cost-effective MINFLUX microscope. He collaborates internationally, with a focus on nuclear pore complexes and artificial intelligence applications in microscopy. 2024 contributions highlight advancements in 3D MINFLUX excitation, dynamic structural biology, and synaptonemal complex analysis in C. elegans. His projects often involve computational modeling and high-throughput imaging. Research Trends Recent publications emphasize super-resolution techniques (MINFLUX, SMLM), PSF inverse modeling for microscope calibration, and nuclear pore complex dynamics. Subfields include apoptosis mechanisms, clathrin coat bending, and AI-driven image analysis. Academic Engagement He participated in the Dies Academicus event at the University of Vienna in 2024, indicating active involvement in academic community activities.

• Machine Learning
• Reinforcement Learning
• Plasma Physics Simulations
• +1 more

Fabian Paischer is a Researcher at the Institute for Machine Learning, Johannes Kepler University Linz (JKU), holding a Doctorate and Master of Science degree. His work bridges machine learning with high-impact scientific domains including plasma physics for fusion energy and reinforcement learning systems. Education: Doctorate (Dr.) Master of Science (MSc) His research focuses on developing neural surrogate models for plasma turbulence simulations and advancing reinforcement learning through pre-trained model modulation and human-readable memory architectures. This interdisciplinary work integrates deep learning with computational physics and autonomous decision-making, targeting applications in sustainable energy and intelligent systems where interpretability and data efficiency are critical. Analysis of his 2023-2025 publications reveals two dominant research thrusts: (1) neural operator applications for plasma edge simulations requiring long-term predictive accuracy in fusion environments, and (2) modular reinforcement learning frameworks enabling knowledge transfer between pre-trained models. These directions highlight his commitment to solving complex scientific computing challenges through novel AI methodologies. Dr. Paischer actively participates in academic communities, including the ELLIS Doctoral Symposium (2021), and maintains ongoing research output through preprints and conference publications at venues like NeurIPS and ICLR workshops.