Summary
Carl Retzlaff is a Postdoctoral Researcher and applied AI specialist with nine years of experience bridging explainable AI, optimization, and full-stack development to real-world environmental and aerospace systems. He holds an MSc from RWTH Aachen (distinction) and a PhD from BOKU Vienna where he built multi-objective optimization frameworks and AI-driven monitoring tools for sustainable forestry. Currently at UniBW München he researches Manned-Unmanned Teaming, focusing on human-in-the-loop methods and explainable decision-making for intelligent flight systems. His background spans reinforcement learning, genetic and heuristic optimization, Kubernetes/GPU deployment, and production-oriented data applications, enabling him to move models from lab prototypes to stakeholder-facing dashboards. Notably, he has combined digital pathology xAI research with large-scale forestry simulations, showing a rare ability to transfer explainability techniques across domains. Based in Munich, he blends rigorous academic work with hands-on engineering to deliver interpretable, deployable AI solutions.
8 years of coding experience
Doctor of Science, Applied Computer Science, Completed with distinction, Doctor of Science, Applied Computer Science, Completed with distinction at University of Natural Resources and Life Sciences, Vienna (BOKU)
Master of Science - MS, Computer and Communication Science, Completed with distinction (1.0 master thesis,1.3 overall), Master of Science - MS, Computer and Communication Science, Completed with distinction (1.0 master thesis,1.3 overall) at RWTH Aachen University
Universitat Politècnica de València
German, English, Spanish