Internship / Thesis in Robotic Foundation Models for Automotive Final Assembly (m/f/d)

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Advanced AI Robotics Opportunity at Volkswagen AG in Wolfsburg

We present a comprehensive and high-impact opportunity for master’s students seeking to specialize in robotics, artificial intelligence, and smart manufacturing systems. This internship or thesis role focuses on the development and application of robotic foundation models within automotive final assembly, one of the most complex and demanding environments in industrial production.

Our work is situated within the Group IT division for Production and Logistics, where we design and deploy intelligent software systems across the full automotive manufacturing value chain. By integrating advanced AI methods with industrial robotics, we are shaping the next generation of autonomous assembly systems capable of operating in dynamic, real-world conditions.

Role Overview: Robotic Foundation Models in Automotive Manufacturing

We focus on bridging the gap between machine learning theory and real-world robotic applications. This role allows candidates to explore how learning-based control systems can be applied to robotic manipulation tasks in automotive final assembly.

Automotive final assembly involves:

High variability in parts and configurations
Strict precision and safety requirements
Continuous interaction between humans and machines

Traditional rule-based robotics often struggles in such environments. Our approach introduces data-driven intelligence, enabling robots to learn from demonstrations, adapt to changes, and improve performance over time.

Participants in this program contribute to:

Designing and validating AI-driven robotic systems
Translating research concepts into industrial solutions
Developing scalable learning pipelines for robotic manipulation

Core Responsibilities and Technical Scope

Collaborative Robot Setup and Experimental Deployment

We begin with the physical setup and configuration of collaborative robotic systems. These include robotic arms equipped with advanced sensors such as:

RGB-D cameras for vision-based perception
Force and torque sensors for tactile feedback
Motion tracking systems for precise positioning

Candidates will work directly with these systems, ensuring proper calibration, safety compliance, and integration into experimental environments that replicate automotive assembly processes.

Research and Analysis of Learning-Based Methods

A strong emphasis is placed on scientific research. We investigate current advancements in:

Imitation learning for robot training from demonstrations
Reinforcement learning for autonomous decision-making
Vision-language models for contextual task understanding

We critically analyze academic literature and adapt relevant methods to industrial use cases, ensuring both theoretical rigor and practical feasibility.

Development of Deep Learning Models for Robotic Control

We design and implement machine learning models using frameworks such as PyTorch. These models are trained to:

Interpret sensor inputs
Predict control actions
Execute complex manipulation tasks

The focus is on end-to-end learning systems that can generalize across different assembly scenarios. Candidates will develop pipelines that connect data collection, model training, and deployment on physical robots.

Industrial Data Collection and Dataset Engineering

Data is central to robotic learning. We design structured processes for:

Capturing demonstration data from human operators or scripted behaviors
Annotating and organizing datasets for training
Managing large-scale data pipelines under industrial constraints

This ensures that models are trained on high-quality, representative data that reflects real-world conditions.

Model Evaluation and Performance Benchmarking

We define and implement evaluation criteria to assess the effectiveness of trained models. These include:

Task completion success rates
Precision and repeatability metrics
Robustness to environmental variations

Evaluation is performed both in simulation and on real robotic systems, providing comprehensive validation of model performance.

Documentation and Scientific Communication

We produce detailed documentation of all work conducted. This includes:

Technical reports outlining methodologies and results
Presentations for internal stakeholders and research teams
Academic thesis documents for university requirements

Clear and structured communication is essential for ensuring the transferability and scalability of developed solutions.

Academic Background and Technical Requirements

We seek highly motivated master’s students from disciplines including:

Computer Science
Robotics
Electrical Engineering
Mechanical Engineering

Candidates must demonstrate strong academic performance and a deep understanding of scientific methodologies.

Required Technical Skills

Solid knowledge of machine learning and neural networks
Practical experience with PyTorch in research or applied projects
Proficiency in Python programming
Familiarity with Linux-based systems

Preferred Experience

Additional experience in the following areas is advantageous:

Robot Operating System (ROS or ROS 2)
Simulation platforms such as Isaac Sim, MuJoCo, or Gazebo
Fundamentals of robotics, including kinematics and control systems

Language Proficiency

Candidates must possess advanced proficiency (C1 level) in either English or German to ensure effective collaboration and documentation.

Application Requirements and Documentation

Applicants must submit a complete set of documents, including:

A tailored cover letter outlining motivation and relevant experience
A detailed curriculum vitae
Proof of current university enrollment
Academic transcripts showing recent performance
Confirmation of mandatory internship (if applicable)
Valid work authorization for non-EU applicants

A well-prepared application demonstrates both technical competence and alignment with the program’s objectives.

Working Conditions and Employee Benefits

We offer a structured and supportive work environment designed to foster innovation and professional growth.

Work Structure

Full-time engagement with a 35-hour work week
Fixed-term contract aligned with internship or thesis duration
Flexible working hours to support productivity and balance

Remote and On-Site Work

Up to 40 percent remote work
Access to modern facilities in Wolfsburg for on-site experimentation

Additional Benefits

30 days of annual leave, plus additional holidays at year-end
Comprehensive training and professional development programs
Access to company vehicle leasing and purchase programs
Bicycle leasing initiatives
Long-term benefits such as pension contributions
Opportunities for sabbatical leave

These benefits reflect a commitment to both professional excellence and personal well-being.

Compensation and Career Development

Compensation is aligned with internal pay structures and varies based on qualifications and experience. Exceptional candidates may qualify for higher salary tiers within the defined framework.

Beyond financial compensation, this role offers significant career advantages:

Direct experience with industrial robotics systems
Exposure to cutting-edge AI research and development
Opportunities to collaborate with interdisciplinary teams

Participants gain practical expertise that is highly valued in industries such as:

Automotive manufacturing
Robotics engineering
Artificial intelligence research
Industrial automation

Research and Development Workflow

The development of robotic foundation models follows a structured pipeline that integrates data, learning, and deployment.

flowchart TD
    A[Sensor Data Acquisition] --> B[Data Structuring and Annotation]
    B --> C[Model Training using Deep Learning]
    C --> D[Simulation-Based Validation]
    D --> E[Deployment on Physical Robots]
    E --> F[Performance Evaluation]
    F --> G[Continuous Optimization]

This iterative workflow ensures that models are continuously improved based on real-world feedback, enabling robust and scalable solutions.

Strategic Importance of AI in Automotive Final Assembly

Automotive final assembly is undergoing a transformation driven by digitalization and artificial intelligence. Key challenges include:

Increasing product customization
Rising complexity of assembly processes
Demand for higher efficiency and quality

By integrating AI-driven robotics, we address these challenges through:

Adaptive automation systems
Data-driven decision-making
Enhanced collaboration between humans and machines

This internship positions candidates at the forefront of this transformation, providing insights into the future of manufacturing.

Location and Organizational Context

The position is based in Wolfsburg, the headquarters of Volkswagen AG. The IT and Digitalization department plays a critical role in shaping the company’s transition toward smart, connected, and autonomous production systems.

Working within this environment offers access to:

Advanced research facilities
Industry-leading expertise
Real-world production environments

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Conclusion

We offer a unique opportunity to engage in cutting-edge research and development at the intersection of robotics and artificial intelligence. This internship or thesis program provides hands-on experience with real robotic systems, enabling candidates to develop solutions that directly impact automotive production.

By combining theoretical knowledge with practical implementation, participants contribute to the creation of next-generation autonomous assembly systems. This role is ideal for individuals who are driven by innovation, possess strong technical skills, and are committed to advancing the future of intelligent manufacturing.