PhD in Probabilistic Deep Learning / Doktorarbeit im Bereich „Probabilistic Deep Learning“(m/w/x)

Description

You will advance the state of the art in Bayesian deep learning by developing trustworthy AI systems and uncertainty-aware models for safety-critical aerospace applications.

Requirements

• •Master’s Degree in computer science or equivalent
• •Strong background in mathematics and statistics
• •Passion for Python/PyTorch and deep-learning implementation
• •Background in probabilistic or Bayesian deep learning
• •Fluency in English
• •Compliance risk awareness and integrity commitment
• •Submission of cover letter, CV, and transcripts

Tasks

• •Develop uncertainty-aware neural networks and probabilistic models
• •Research Bayesian inference techniques for deep learning
• •Establish methodological foundations for trustworthy AI systems
• •Design robust models for uncertainty quantification
• •Analyze epistemic and aleatoric uncertainty in networks
• •Implement aerospace-relevant benchmarks and safety metrics
• •Design scalable Bayesian architectures for embedded hardware
• •Conduct empirical studies on predictive uncertainty calibration
• •Develop diagnostic tools for aerospace risk profiles
• •Characterize and monitor model operational applicability domains
• •Explore active learning strategies to expand safety margins

Tools & Technologies

Benefits