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PhD in Probabilistic Deep Learning / Doktorarbeit im Bereich „Probabilistic Deep Learning“(m/w/x)
Developing uncertainty-aware neural networks and Bayesian inference for trustworthy AI systems in aerospace research. Strong background in mathematics, statistics, and probabilistic deep learning required. 35-hour week, flexitime.
Anforderungen
- 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
Aufgaben
- 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
Ausbildung
- Bachelor-AbschlussODER
- Master-Abschluss
Sprachen
- Englisch – verhandlungssicher
Tools & Technologien
- Python
- PyTorch
Benefits
Flexibles Arbeiten
- Flexitime
- Work-life balance
- Flexible working arrangements
Attraktive Vergütung
- Attractive salary
Lockere Unternehmenskultur
- International environment
Karriere- und Weiterentwicklung
- Global networking opportunities
Abwechslungsreiche Aufgaben
- Modern and diversified technologies
Gemeinnützige Ausrichtung
- Generation Airbus Community participation
Mentoring & Coaching
- Personal growth support
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PhD in Probabilistic Deep Learning / Doktorarbeit im Bereich „Probabilistic Deep Learning“(m/w/x)
Developing uncertainty-aware neural networks and Bayesian inference for trustworthy AI systems in aerospace research. Strong background in mathematics, statistics, and probabilistic deep learning required. 35-hour week, flexitime.
Anforderungen
- 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
Aufgaben
- 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
Ausbildung
- Bachelor-AbschlussODER
- Master-Abschluss
Sprachen
- Englisch – verhandlungssicher
Tools & Technologien
- Python
- PyTorch
Benefits
Flexibles Arbeiten
- Flexitime
- Work-life balance
- Flexible working arrangements
Attraktive Vergütung
- Attractive salary
Lockere Unternehmenskultur
- International environment
Karriere- und Weiterentwicklung
- Global networking opportunities
Abwechslungsreiche Aufgaben
- Modern and diversified technologies
Gemeinnützige Ausrichtung
- Generation Airbus Community participation
Mentoring & Coaching
- Personal growth support
Gefällt dir diese Stelle?
BetaDein Career Agent findet täglich ähnliche Jobs für dich.
Über das Unternehmen
Airbus Defence and Space GmbH
Branche
Aerospace
Beschreibung
Airbus is committed to achieving workforce diversity and creating an inclusive working environment.
Noch nicht perfekt?
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