PhD/Postdoc position (m/f/d): AI for Safety-Critical Systems
At the Autonomous Multisensor Systems (AMS) group

Job description:

Tasks:

• Conduct independent research on the safe integration of AI into safety-critical systems in close collaboration with the research team and project partner, addressing the research objectives below.
  
• Maintain regular communication and coordinate research activities with the project partner.
  
• Support project management activities and contribute to project reports.
  
• Document, publish, and present research results.
  

Research objectives:

• Analyze functional safety requirements and translate them into test scenarios, evaluation metrics, and data requirements.
  
• Develop and evaluate AI models for the sensor-based classification of safety-relevant operating states.
  
• Investigate and compare uncertainty quantification methods, including probabilistic models such as Gaussian process regression, ensemble methods, and conformal prediction.
  
• Define operating states and decision boundaries and develop safe switching strategies between AI-based and physics-based models.
  
• Investigate physics-informed machine learning approaches and their potential contribution to defining safe operating regions.
  
• Develop and evaluate safety-envelope and runtime-assurance concepts for hybrid AI systems.
  
• Investigate suitable verification and validation methods for the developed AI and hybrid-system concepts.
  

Requirements profile:

• M.Sc. (or equivalent) in Computer Science, Electrical Engineering, Data Science, Systems Engineering or a closely related field.
  
• Good knowledge of machine learning, signal processing, data analysis, or related methods, and strong programming skills in Python and/or MATLAB.
  
• Excellent written and spoken English; German language skills are an advantage.
  
• Ability to conduct independent research and collaborate effectively with an interdisciplinary team and project partners.
  
• Strong interest in research combining AI, sensor data processing, and safety-critical systems.
  
• Willingness to publish research findings and present them at conferences.
  

Desirable qualifications:

• Knowledge of system identification and modeling.
  
• Knowledge of signal and sensor data processing and filtering techniques.
  
• Experience with machine learning, ideally including probabilistic models.
  
• Knowledge of uncertainty quantification methods.
  
• Familiarity with state estimation, safety-critical systems, or verification and validation methods is an advantage
  

Your benefits:

• The opportunity to pursue a Ph.D., supported by academic supervision and mentoring.
  
• A supportive, collegial team with an open culture of communication, where everyone’s voice is valued and initiative, creativity, and new ideas are welcome.
  
• Research support services and Graduate Academy programs to support your personal and professional development.
  
• An excellent working environment with a wide range of facilities on campus (canteen, further education opportunities, sports facilities, childcare, etc.).
  
• Attractive salary package according to TV-L (pay group 13) with occupational pension scheme (VBL) and 30 days of vacation per year