PhD Candidate - Advanced Predictive Modeling and Machine Learning for Automotive Components (m/f/d

YOUR TEAM

The aim of our PhD Program is to promote innovative topics that are relevant to CARIAD. We cooperate with top universities and bring new research projects to life. Our PhD candidates get the opportunity to create new innovations in their projects for CARIAD and the respective scientific field. All PhD projects are accompanied by a supervising professor and a dedicated CARIAD mentor. Our PhD candidates further benefit from development programs and a strong academic exchange within the internal PhD network. The successful candidate will take on a research project in Physics-Informed Machine Learning. The goal of this dissertation is to explore, experiment with, and evaluate state-of-the-art methods, identifying the most effective and innovative approaches and processes for application within the relevant industry. 

We offer an exciting PhD opportunity within our Data Engineering team. Our team builds scalable systems for Predictive Maintenance and is currently expanding into AI agents, LLM-based workflows, and intelligent data services for simulation. You will work with cutting-edge technologies and collaborate closely with experts in data engineering, artificial intelligence, and machine learning to help shape the future of automotive AI. 

WHAT YOU WILL DO

• Analyze and prepare large-scale automotive datasets  
• Generate actionable insights from data-driven research 
• Visualize and present findings using advanced data visualization tools 
• Develop machine learning algorithms to detect and predict failures in automotive components 
• Adapt and optimize models for performance under real-world constraints 
• Define validation metrics and implement validation workflows for prediction models  
• Collaborate closely with data scientists and engineering teams 

WHO YOU ARE

• Master’s degree in data science, physics, electrical engineering, mathematics, or related field
• Strong foundation in machine learning, statistics, and data analysis
• Proficient in Python, Pyspark, R, and SQL for data-driven applications
• Hands-on experience with large-scale data analysis
• Familiarity with Databricks or similar data engineering platforms
• Background in high-voltage batteries or other automotive components a plus
• Strong analytical and communication skills; fluent in English
• Enthusiastic about predictive maintenance solutions  

NICE TO KNOW

• Duration: 3 years 
• Working with high-ranked University
• Possibility to supervise students
• Remote work options
• Temporary work from abroad in selected countries
• 30 days paid leave
• Special Events e.g. PhD-Day (Doktorandentag), Trainings
• Note: Please attach a transcript of records including a module overview to your application via the system after you have submitted your application. Otherwise the application cannot be processed
• Important: The PhD admission requirements are set by the university and candidates have to fulfill these requirements before starting their projects. Candidates need a confirmation by the supervisor professor before onboarding (not necessary for application)
• If you have further questions about the candidate journey at CARIAD, please contact us: careers@cariad.technology