Portrait of Nico Jungbauer wearing a dark suit and blue tie

Physicist and AI Expert

Nico Jungbauer

Scientific depth meets technical delivery – at the intersection of computer vision, maritime technology and responsible AI.

Project Lead at TKMS ATLAS ELEKTRONIK GmbH · PhD Candidate at the University of the Bundeswehr Munich

01 · Profile

About Me

My focus is on leading interdisciplinary research projects and developing maritime AI systems – from technical concepts to coordinating teams and project partners.

02 · Responsibility

Project Lead

Leading a publicly funded project focusing on maritime defence technology. Responsible for resource planning, stakeholder management, and technical milestone delivery.

03 · Research

AI Specialist

Research at the Chair of Computer Vision. Focus: developing uncertainty-aware approaches and explainability methods for modern computer vision architectures.

04 · Background

Physicist

Physical modelling, data-driven analysis and systems thinking form the methodological foundation of my work. I apply that depth to robust computer-vision and explainable-AI systems for technical applications.

Publications & intellectual property

Work on maritime perception, explainable AI, uncertainty modelling and risk-aware autonomous navigation.

Patent applications

Five patent applications filed

All five pending, not yet granted · sole inventor on two applications
Topics of the five applications
  • Autonomous underwater vehicle for object detection and identification (with L. Daibert)
  • Signal processing unit for object classification (sole inventor)
  • Method and apparatus for maritime object classification (sole inventor)
  • Assistance system for risk assessment of semi-autonomous watercraft (with M. Tepe)
  • AI weighted Training (with M. El Yahyaoui)
Journal article

Maritime vision datasets for autonomous navigation: A comparative analysis

N. Jungbauer, H. Huang, H. Mayer
Maritime Technology and Research 7(4), 277976 · 2025
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Conference paper

Correlating Explainability and Predictive Uncertainty under Controlled Perturbations for Maritime Object Detection

N. Jungbauer et al.
GCPR 2026 · Main Track · accepted for publication
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Conference paper

Quantitative Risk Assessment for Maritime Autonomous Navigation Using AI Perception Metrics and Bayesian Networks

ESREL 2026
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Contact

Let’s talk.

For professional exchange on computer vision, explainable AI, maritime autonomy and technical project leadership.