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PhD position within the AI team at Airbus Central Research and Technology, with a focus on advanced research in probabilistic and Bayesian deep learning. The overarching objective is to advance the theoretical and practical state of the art in Bayesian deep learning and uncertainty quantification (UQ) by designing and rigorously validating robust probabilistic models. The ultimate aim is to enable verifiable safety guarantees and trustworthy deployment of AI systems in safety-critical aerospace applications.
Job Responsibility:
Principled modeling of uncertainty
Domain-specific evaluation frameworks
Scalable Bayesian architectures
Rigorous empirical validation of uncertainty quality
Operational Applicability Domain (OAD)
Requirements:
Master’s Degree in the area of computer science, or any equivalent field of studies with strong computer science / machine learning relations (e.g. mathematics, physics, engineering)
A strong background in mathematics and statistics is highly desirable
A passion for programming in Python/PyTorch and the ability to implement complex deep-learning algorithms efficiently is a valued asset
Fluency in English is required
Nice to have:
A background in probabilistic deep learning, Bayesian deep learning, or uncertainty quantification is a plus
What we offer:
Attractive salary and work-life balance with a 35-hour week (flexitime)
International environment with the opportunity to network globally
Work with modern/diversified technologies
Opportunity to participate in the Generation Airbus Community to expand your own network