Machine Learning Certification Researcher

About this role

About the Role Imagine pioneering the mathematical and logical foundations that enable AI to fly safely in real-world, safety-critical aviation. As a Machine Learning Certification Researcher, you will address one of the most fundamental challenges in modern AI: proving that ML models are safe, reliable, and mathematically sound for deployment in autonomous flight. Your work will bridge the gap between cutting-edge statistical learning theory and formal aviation certification frameworks, building upon previous work (EASA CoDANN I and II, and FAA report DOT/FAA/TC-21/48), and putting real-world systems to the test.

At Destinus, we are shaping the future of defense through high-speed, autonomous flight technologies. We develop next-generation unmanned aerial systems that deliver greater speed, precision, and efficiency, supporting governments and defense organizations in mission-critical operations worldwide. Daedalean AG, the AI and autonomy hub of the Destinus Group, plays a key role in this vision—developing safety-critical AI systems at the intersection of advanced research and real-world aviation.

Enabling machines to perceive, understand, and make decisions in real time, we combine aerospace engineering and artificial intelligence to build the next generation of autonomous flight systems, driving innovation where it matters most. What You'll Do • AI Certification Strategy: Analyze our existing AI certification strategy and help adapt it—or pioneer entirely new strategies—to certify different supervised ML. • EASA Framework Alignment: Connect mathematical guarantees, technical arguments, and model evaluations to the EASA W-shaped process for AI certification in aviation, ensuring our research aligns with the latest regulatory guidelines.

• Theoretical Foundation & Modeling: Conduct research into statistical learning theory, generalization bounds, and mathematical guarantees for deep neural networks operating under dynamic real-world conditions. • Uncertainty Quantification: Apply statistics and stochastic processes to model environmental uncertainties and sensor noise, and to quantify confidence in performance metrics. • Literature & Proof Evaluation: Critically evaluate academic literature, mathematical proofs, and engineering assumptions with peer-review rigor to identify safety limitations and theoretical gaps in state-of-the-art AI.