206/2026 PhD/Postdoc position (m/f/d): AI for Safety-Critical Systems At the Autonomous Multisensor

At the Otto von Guericke University Magdeburg is following position available, subject to funding approval:

# PhD/Postdoc position (m/f/d): AI for Safety-Critical Systems

At the Autonomous Multisensor Systems (AMS) group

Salary scale: 13 TV-L

Date commencing: as soon as possible

Duration: 24 months

Contract status: 100 %

The Autonomous Multisensor Systems Group at Otto von Guericke University Magdeburg develops advanced methods for sensor data processing and multisensor data fusion, with applications in autonomous mobility, robotics, and industrial process automation. Our research covers signal and image processing, state estimation, navigation, path planning, and machine learning.

Join our team and contribute to the new research project “Adaptive Safety Switching and Uncertainty-Responsive AI”, in which we develop innovative methods for the safe integration of artificial intelligence into safety-critical systems. You will investigate how sensor data can be used to reliably assess the state of a system, quantify the uncertainty of AI-based decisions, and enable safe switching between different operating modes. Your research will also address the verification and validation of hybrid AI systems, combining machine learning and probabilistic modeling with rigorous safety analysis.

More information about our group is available at [ams.ovgu.de](https://ams.ovgu.de/).

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

For further information about the position, please