Doctoral Thesis: Extension of safety methodologies for CCAM systems across the complete lifecycle, considering AI system requirements (f/m/div)

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We are offering a doctoral thesis dealing with the \“Extension of safety methodologies for CCAM systems across the complete lifecycle, considering AI system requirements.\“ Artificial Intelligence (AI) technologies are expected to yield diverse economic and societal benefits across various sectors, including the environment, health, public sector, finance, mobility, home affairs, and agriculture. In the mobility domain, cooperative, connected, and automated mobility (CCAM) systems benefit from the increased utilization of AI systems (e.g., for perception tasks), which is expected to continue growing in the next few years. However, the validation of such CCAM systems is still considered an open research topic concerning both the respective processes and relevant safety-related metrics to determine CCAM deployment adequately. Additionally, these systems require ongoing monitoring beyond the initial deployment, as they will be updated regularly. With the increased utilization of AI, additional safety and security risks emerge. The EU AI Act regulates high-risk AI systems, ensuring they are only deployed within Europe if they are trustworthy and human-centric. In this sense, the PhD thesis will focus on methodologies, concepts, and approaches to enhance current safety methodologies for CCAM systems by considering AI systems. Regarding such AI systems, trustworthiness (including lawful, ethical, and robustness aspects) must be achieved in compliance with the AI Act and other relevant standards and legislation. Furthermore, these methodologies must be applicable for the initial deployment and extend across the entire AI lifecycle, including the monitoring phase. Concretely, metrics for generating safety and trustworthy-related evidence should be formulated. To formulate such metrics, the operational design domain (ODD) and behavior competency (BC) concepts from the automated driving domain can be utilized in risk assessment strategies. The research will be performed in the Enablers & Emerging Trends group within the Research and Technology Funding department at Infineon Technologies AG, Neubiberg.
The research is carried out in cooperation with the University of Granada for the PhD degree, under the supervision of Diego Pedro Morales Santos, Full Professor, Ph.D., Department of Electronic and Computer Technology.

Job Description
The tasks within the thesis will consist of:

• Exploration of AI trustworthiness assurance concepts and methods focusing on safety and technical robustness.
• Investigation and development of safety and trustworthiness assurance concepts, methods and approaches, for example, regarding the consideration of residual risks across the AI lifecycle.
• Development of an assurance process considering the entire AI lifecycle as part of an overall AI-based CCAM system.
• Design of safety argumentation approaches generating relevant evidence for safety and robustness using emerging concepts, e.g. operational design domain and behavior competencies.

The learnings out of the thesis will lead to:

• Scientific research studies on concepts, methods, and approaches are needed to deploy AI-based CCAM systems compliant with the EU AI Act and other relevant standards and regulations.
• Collaboration with University researchers and PhDs at Infineon performing their research in AI and its related applications, e.g., environment perception, and cooperation in European and German-funded projects with industrial and academic partners.
• Set up demonstration solutions to showcase and validate safety and trustworthiness assurance concepts.

Your Profile
A doctoral student is a research enthusiast,
…whose interests are scientific research combined with the passion for Infineon’s innovative products and applications.
…who enjoys working in an industrial environment in combination with an Infineon partner university.
…who appreciates open communication and the contribution of an international environment.
…and is thus an excellent candidate for a further academic or industrial career after completion of their thesis.

As the ideal candidate you:

• Are eligible for full-time PhD studies.
• Must have a university master\’s degree in computer science, electrical engineering, or equivalent, focusing on embedded systems, data mining, and machine learning for smart systems.
• Basic knowledge about safety and trustworthy concepts (e.g. for automated vehicles).
• Experience with AI algorithms, approaches and concepts within different applications.
• Basic knowledge of system theory, statistics and probability theory.
• Experience in demo system set-up, e.g. lab demonstrators at university.
• Relevant industry experience, fluent English and basic German skills.

Know-how in following topics is preferable – but not mandatory:

• Knowledge of functional safety (ISO 26262), SOTIF (ISO 21448), and ISO 8800 (Road vehicles – Safety and artificial intelligence)
• Basic systems engineering knowledge
• Basic knowledge of the operational design domain (ODD) concept
• Basic knowledge about fault diagnosis and scenario design
• Basic knowledge about simulation frameworks used for virtual validation approaches

Contact:
Britta Johansson

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