Job Description:
In order to support Airbus Central Research & Technology , Airbus Defence and Space is looking for a
PhD in Probabilistic Deep Learning (d/f/m)
Are you motivated to help bring deep learning into safety-critical real-world applications? If so, we encourage you to apply.
We are offering a PhD position within the AI team at Airbus Central Research and Technology, with a focus on advanced research in probabilistic and Bayesian deep learning. Deploying deep learning in safety-critical domains requires more than strong predictive performance: it demands well-calibrated uncertainty, principled treatment of model confidence, and methods that remain reliable under distributional shift.
In this role, you will contribute to the development and rigorous evaluation of uncertainty-aware neural networks, Bayesian inference techniques, and probabilistic models. Your work will help establish the methodological foundations for trustworthy AI systems that can be deployed responsibly in high-stakes aerospace settings. This position offers a distinctive opportunity to shape the next generation of probabilistic deep learning methods for safety-critical applications.
Start: as soon as possible
Duration: 3 years
Your location:
Our site is just a stone's throw away from Munich, the beautiful capital of Bavaria. Are you into sports and other outdoor activities? The Alps and Lake Starnberg are within an hour’s reach, offering a multitude of recreational options.
Your benefits:
- 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.
Scope of PhD thesis:
The overarching objective of this PhD thesis 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.
This objective will be pursued along the following research axes:
- Principled modeling of uncertainty: Develop and analyse methods for representing epistemic and aleatoric uncertainty in deep neural networks, with a particular focus on reliable UQ and robust out-of-distribution (OOD) detection in high-dimensional perception and decision-making tasks.
- Domain-specific evaluation frameworks: Define and implement aerospace-relevant benchmarks, scenarios, and metrics to assess robustness, reliability, and safety compliance of probabilistic models, with an emphasis on traceability of assumptions and alignment with emerging certification requirements.
- Scalable Bayesian architectures: Design novel deep learning architectures and approximate Bayesian inference schemes that are more computationally efficient and scalable than traditional UQ methods, thereby enabling deployment on certified embedded hardware with strict real-time and resource constraints.
- Rigorous empirical validation of uncertainty quality : Conduct comprehensive empirical studies focusing on calibration, reliability, and sharpness of predictive uncertainty. This includes developing and analysing new metrics and diagnostic tools tailored to aerospace risk profiles and decision thresholds.
- Operational Applicability Domain (OAD): Investigate methods to infer, characterise, and monitor a model’s operational applicability domain, specifying safe operational boundaries and mechanisms for graceful degradation. Explore strategies such as active learning and targeted data acquisition to systematically expand the OAD while preserving safety margins.
Qualifications:
- 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.
- A background in probabilistic deep learning, Bayesian deep learning, or uncertainty quantification is a plus.
- Fluency in English is required.
- This job requires an awareness of any potential compliance risks and a commitment to act with integrity, as the foundation for the Company’s success, reputation and sustainable growth.
Please upload the following documents: cover letter, CV, relevant transcripts, enrollment certificate.
Not a 100% match? No worries! Airbus supports your personal growth.
Take your career to a new level and apply online now!
This job requires an awareness of any potential compliance risks and a commitment to act with integrity, as the foundation for the Company’s success, reputation and sustainable growth.
Company:
Airbus Defence and Space GmbH
Employment Type:
PHD, Research
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Experience Level:
Student
Job Family:
Research and Technology
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