Stefan Badelt

Stefan Badelt holds a PhD and Magister degree in Theoretical Chemistry, currently active within the Faculty of Chemistry at an Austrian institution (likely University of Vienna based on academic conventions). His research spans computational RNA biology, bioinformatics, and molecular engineering with continuous publication output from 2011 through projected 2025 activities.

• RNA Folding (100%)
• Protein Secondary Structure (67%)
• Transcription Processes (45%)
• Circular RNA Mechanics
• Alu Element Biology

His research focuses on cotranscriptional RNA folding dynamics, developing computational tools like DrForna for visualization and DrTransformer for heuristic folding prediction. Recent work critically examines deep learning limitations in RNA structure prediction while advancing nearest-neighbor energy models. His fingerprint reveals strong specialization in RNA polymerase II dynamics and non-pseudoknotted structural systems.

Publication trends show consistent output in high-impact bioinformatics journals, with 2022-2023 featuring significant algorithmic contributions. His work bridges computational methods with experimental validation, particularly in RNA folding pathways and DNA strand-displacement systems.

While no specific awards are documented, his publications demonstrate substantial scholarly impact with multiple papers exceeding 10 Mendeley readers and significant citation metrics.

Badelt actively mentors students through collaborative projects, evidenced by co-authored publications with Laurenz Miksch and others. His research receives support through computational biology grants enabling development of tools like the commit-and-delay model for RNA dynamics. Future work includes domain-level design of cotranscriptional folding pathways presented at upcoming 2024-2025 conferences.

He operates within the Theoretical Chemistry research ecosystem, collaborating closely with Ivo Hofacker's group on RNA bioinformatics. Current activities focus on exact coarse-grained dynamics modeling and pseudoknot-free pathway design, positioning his lab at the intersection of computational biology and molecular engineering.