PhD Student Data Analytics Automated Driving (f/m/d)

PhD Student Data Analytics Automated Driving (f/m/d)

Job ID:  24265
Company:  Volkswagen AG
Location: 

Wolfsburg, DE, 38436

Department:  Research and Development
Career Level:  PhD students
Working Model:  Full-time
Contract Type:  Fixed-term
Remote Working:  Up to 40%
Posting Date:  May 13, 2026

PhD Student Data Analytics Automated Driving (f/m/d)

Work environment

Our department “Automated Driving and Parking” is responsible for the series development of automated driving functions at SAE Level 2 hands-free and SAE Level 4 “Highway Pilot” for Volkswagen at the complete vehicle level. The comprehensive and systematic verification and validation of these systems are carried out through a combination of extensive real vehicle data, safety-critical evaluation methods, and direct integration with development and release processes. In this doctoral position, you will work at the interface of data science, automated driving, and system validation – with direct relevance to production vehicles and large-scale fleet data. This allows you to make a measurable contribution to the safety of automated driving functions using real vehicle data. From the very beginning, you will be involved in the validation and release of modern driver assistance and automated driving functions and will develop data-driven methods to systematically identify, assess, and translate real driving scenarios into robust safety cases. You will work in an interdisciplinary team of engineers and data science experts and engage in close technical exchange across team and disciplinary boundaries.

Possible Tasks within this Role

  • Analyzing large-scale real-world driving datasets to identify safety-critical driving episodes
  • Detecting, segmenting, and structuring driving scenarios relevant to the operational domain of automated driving functions
  • Enriching identified scenarios with relevant environmental, infrastructure, and system attributes
  • Developing a machine-readable, data-driven scenario catalog
  • Deriving safety metrics and contributing to evidence-based validation and release decisions

 

The final doctoral topic is defined together with the professor. The prerequisite for the cooperation is the confirmation of supervision as well as a confirmed delimitation of topics by the professor of a university or research institution entitled to confer a doctorate.

Qualification requirements

  • Good to very good university degree qualifying for doctoral studies in computer science, data science, systems engineering, robotics, or a comparable field of study
  • Strong knowledge of data analysis and machine learning, ideally with experience in time series or sequential data
  • Experience with statistical analysis as well as clustering or classification methods
  • Basic understanding of complex technical systems, ideally in the context of automated or cyber-physical systems
  • English language level C1, German language level B1
  • Analytical and structured working style
  • High level of self-motivation for scientific research
  • Ability to work in an interdisciplinary environment

The following documents must be submitted with the application

  • Curriculum Vitae
  • Current transcript of records / Master’s or Diplom degree certificate

Keywords

Dissertation, Advanced driver assistance systems (ADAS), vehicle safety, scenario analysis, fleet data analysis, vehicle data analysis, operational domain modeling


What we offer

  • Attractive salary & 30 vacation days (+ 24.12. and 31.12. off)
  • 35-hour week, flexible working hours, remote work
  • Special conditions for the purchase and leasing of vehicles
  • Free seminars on scientific work and interdisciplinary qualifications
  • Participation in the doctoral network for scientific exchange with science representatives and other doctoral candidates within the Volkswagen Group

Contact person for this posting: Fabian Wenzel

Work environment

Our department “Automated Driving and Parking” is responsible for the series development of automated driving functions at SAE Level 2 hands-free and SAE Level 4 “Highway Pilot” for Volkswagen at the complete vehicle level. The comprehensive and systematic verification and validation of these systems are carried out through a combination of extensive real vehicle data, safety-critical evaluation methods, and direct integration with development and release processes. In this doctoral position, you will work at the interface of data science, automated driving, and system validation – with direct relevance to production vehicles and large-scale fleet data. This allows you to make a measurable contribution to the safety of automated driving functions using real vehicle data. From the very beginning, you will be involved in the validation and release of modern driver assistance and automated driving functions and will develop data-driven methods to systematically identify, assess, and translate real driving scenarios into robust safety cases. You will work in an interdisciplinary team of engineers and data science experts and engage in close technical exchange across team and disciplinary boundaries.

Possible Tasks within this Role

  • Analyzing large-scale real-world driving datasets to identify safety-critical driving episodes
  • Detecting, segmenting, and structuring driving scenarios relevant to the operational domain of automated driving functions
  • Enriching identified scenarios with relevant environmental, infrastructure, and system attributes
  • Developing a machine-readable, data-driven scenario catalog
  • Deriving safety metrics and contributing to evidence-based validation and release decisions

 

The final doctoral topic is defined together with the professor. The prerequisite for the cooperation is the confirmation of supervision as well as a confirmed delimitation of topics by the professor of a university or research institution entitled to confer a doctorate.

Qualification requirements

  • Good to very good university degree qualifying for doctoral studies in computer science, data science, systems engineering, robotics, or a comparable field of study
  • Strong knowledge of data analysis and machine learning, ideally with experience in time series or sequential data
  • Experience with statistical analysis as well as clustering or classification methods
  • Basic understanding of complex technical systems, ideally in the context of automated or cyber-physical systems
  • English language level C1, German language level B1
  • Analytical and structured working style
  • High level of self-motivation for scientific research
  • Ability to work in an interdisciplinary environment

The following documents must be submitted with the application

  • Curriculum Vitae
  • Current transcript of records / Master’s or Diplom degree certificate

Keywords

Dissertation, Advanced driver assistance systems (ADAS), vehicle safety, scenario analysis, fleet data analysis, vehicle data analysis, operational domain modeling


What we offer

  • Attractive salary & 30 vacation days (+ 24.12. and 31.12. off)
  • 35-hour week, flexible working hours, remote work
  • Special conditions for the purchase and leasing of vehicles
  • Free seminars on scientific work and interdisciplinary qualifications
  • Participation in the doctoral network for scientific exchange with science representatives and other doctoral candidates within the Volkswagen Group

Contact person for this posting: Fabian Wenzel

Job ID:  24265
Company:  Volkswagen AG
Location: 

Wolfsburg, DE, 38436

Department:  Research and Development
Career Level:  PhD students
Working Model:  Full-time
Contract Type:  Fixed-term
Remote Working:  Up to 40%
Posting Date:  May 13, 2026

Volkswagen - that's why

We want to revolutionize and shape the mobility of tomorrow sustainably. For this purpose, we are looking for talents who are willing to break completely new ground with us and are not afraid to make mistakes. We are looking for personalities who want to achieve something extraordinary. And talents who believe in the innovative power of big ideas. Are you in?

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