PhD Student AI-Driven Failure Analysis - Fault Classification for the Electric Powertrain

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Apply to Mercedes-Benz and find the area where you can develop your talents individually. You will be supported by visionary colleagues who share your pioneering spirit. Joining us means becoming part of a global team whose goal is to build the most desirable cars in the world. Together for excellence.

Number: MER0003W7K Mercedes-Benz Group AG is one of the most successful automotive companies in the world, known for innovation, quality and luxury. Our research & development area is the heart of our innovative power, where we work on groundbreaking technologies and concepts that shape the future of mobility. Our e-Drive Software System Integration (RD/EDD) department is responsible for the development of functional software for electrical drives and the integration of all software components of the electric drivetrain. These include Flashen-over-the-Air (FOTA) and error management. In the Team Systemintegration eDrive we accompany the software of the electric drivetrain from planning to approval. This includes the appointment, content definition, documentation, commissioning and backup. We work closely with the specialist departments and our global, internal and external IT partners. In your future team, highly motivated and friendly colleagues look forward to your support, because we work for each other and cooperate passionately. A flexible way of working is standard and well established, and we look forward to regular personal meetings. Your tasks: Error analysis is the systematic investigation of failures or problems in the product to identify their causes. In our case, we focus mainly on software errors identified at various test levels such as system tests, software tests and vehicle tests. As soon as an error is identified, it is assigned to a person responsible for determining its cause or forwarding to the corresponding department for more detailed analysis. Often the causes are not directly linked to observable behavior. The automation of this error analysis process and the replacement of this workflow by an AI model for analysis and classification of errors is the core of the work. Causes may come from different software submodules of the same ECU or from different ECUs. Main tasks: You will be part of an interdisciplinary development team and will work on the development and implementation of AI models for fault analysis and classification in drivetrain controllers

You will analyse and evaluate the current Defect Management workflow as well as investigate its optimization potential by AI

The implementation of AI models, the network communication description files, ECU architecture elements and all other required files as inputs take into account the ECU behavior that helps in error analysis, and the training of the model with already existing error analysis data, will form the core of this task

You will interact with other AI experts in the organization to gather ideas and use their expertise to further develop this topic.

Specific research focus: Preprocessing techniques: Methods for cleaning and preparing log and measurement files for AI analysis

AI model development: design and training of machine learning models for analysis and classification of errors

Feature Extraction: Identification of key features from log files and measurement data that indicate specific errors

Classification of errors: Creation of algorithms to assign identified errors to severity levels and the respective teams/themes

Validation and Test: Evaluation of the performance of AI models using real data from test data at different test levels (software, system, vehicle)

Implementation strategy: Proposal for a framework for integration of AI models into existing defect management workflows of drive systems

The activity can begin from February 2026. Adjustment requirement is the support of the doctoral project by a university lecturer. The selection of a corresponding person is the responsibility of the doctoral student.

You have a master's degree in engineering, strategic design or a comparable technical/scientific field in artificial intelligence, machine learning, data analysis or computer science

You like to work in an interdisciplinary team, but also independently, and want to combine scientific work with practical relevance

You have well-founded knowledge of agile organisational development and a high understanding of software development, automotive system knowledge and experience with LLM/GenAI technologies

You can quickly get involved in new complex topics and address unknown facts structured

You are characterized by high commitment and initiative, economic and customer-oriented thinking and acting as well as a high affinity for new technologies

Secure knowledge of German and English in word and writing

Safe handling of MS Office

Commitment and team skills

Hands-on mentality and problem solving skills

Analytical thinking and strategic working

Do you want to make your doctorate in cooperation with Daimler? We offer you an international network of experts, research materials, work insights and personal mentors, who will assist you as a contact person in addition to your faculty. Doctorate at a renowned university with the support of Daimler as a non-academic partner – and use the know-how of a globally active group. Additional information: Doctorate in the Mercedes-Benz Group! Benefit from our know-how, an international expertise network, research materials, work insights and personal support by mentors – in addition to your university. Further information can be found here. We look forward to your online application with CV, Letters and Certificates. Please do not forget to mark your documents as ''relevant for this application' in the online form and note the maximum file size of 5 MB. Disabled and equalized applicants are welcome! The severely disabled representative (sbv-location@mercedes-benz.com) is happy to support you in the application process. HR Services will be happy to help you with questions about the application process. You can reach us by email via myhrservice@mercedes-benz.com or by phone at 0711/17-99000 (Mo-Fr 10-12am & 13-15am).

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