Ph.D. Candidate for AI-Supported Systems Engineering

• Attractive remuneration: 13 monthly salaries, Givve card topped up with 50 each month and optional allowances
• Work-Life balance: 30 days of vacation and a flextime model for flexible working hours
• Home office: Possibility to work from home
• University contact: frequent visit to supervising professor will be supported
• Development: Wide range of trainings and education opportunities
• Employee benefits: Attractive leasing offers, additional private health insurance, free employee advice assistance hotline and much more
• Health: Yearly health & wellbeing days, Urban Sports Club membership
• Attractive working environment: Modern offices, free parking
• International company: Career opportunities in a global environment
• Open company culture: Diverse, multicultural team
• Events: Regular company & team events

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• Master's degree (or equivalent) in Computer Science, Engineering or a related field
• Strong background in artificial intelligence and/or systems engineering
• Programming proficiency (e.g., Python, MATLAB)
• Experience with LLMs using advanced frameworks (e.g., RAG, ReAct) is a plus
• Excellent analytical, problem-solving, and communication skills
• Motivation to work independently and as part of a multidisciplinary team
• Prior research experience and publications are a plus
• Fluency in spoken and written English. Knowledge of German is not required but preferred.

As Ph.D. Candidate (m/f/d) for AI-Supported Systems Engineering you are passionate about developing and applying state-of-the-art artificial intelligence techniques to solve complex challenges in systems engineering, including modeling, design, analysis, and optimization of advanced automotive systems. Our team is dedicated to pioneering innovative solutions at the intersection of AI and systems engineering. The successful candidate will have the opportunity to work on cutting-edge projects, collaborate with leading experts, and contribute to high-impact publications. The research will involve, but is not limited to: * Developing AI-driven tools and methods for systems modeling and simulation * Exploring explainable AI and trustworthy systems engineering: Define process and methodologies to formally verify the outputs generated by AI for the development process * Integrating machine learning algorithms into systems engineering processes * Automating requirements analysis and design verification * Collaborating with DENSO internal experts and industry partners on real-world applications