---
title: "Celebrating Achievements in Dual Studies: Three Promising Talents Join Brain of Materials"
description: Discover how Brain of Materials celebrates the successful completion of three dual studies graduates in Computer Science. These new talents enhance the company's capabilities in AI-driven material processes, fostering innovation and growth.
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# Celebrating Achievements in Dual Studies: Three Promising Talents Join Brain of Materials

#### Practical AI Research as the Foundation for Advancing Data-Driven Material Processes

The targeted promotion of qualified young professionals is a fundamental pillar for sustainable innovation capability in technology-driven companies. Brain of Materials is particularly pleased to announce the successful completion of three dual students who pursued a degree in Computer Science at Hochschule Niederrhein.

All three graduates have successfully completed their Bachelor's degrees, writing their thesis on various applications of Artificial Intelligence (AI), and they have convincingly defended their findings. Following their studies, they will continue their roles at Brain of Materials under subsequent contracts.

#### AI Applications Providing Tangible Value in Material Data

The Bachelor's theses addressed practical, relevant questions within the context of data-driven material and testing processes. Key areas of focus included:

- AI-supported analysis and classification of technical documents
- Automated extraction and structuring of testing and material data
- Intelligent support in the identification and assignment of testing requirements
- Optimization of data quality through adaptive feedback mechanisms

The objective was to link real-world operational scenarios from Brain of Materials with modern methods of Machine Learning and data analytics. The results remarkably demonstrate the potential of systematically integrating AI technologies into industrial data processes.

Through the close alignment of scientific methodology and practical implementation within the company, solutions were developed that can be directly incorporated into the further enhancement of the platform.

#### Dual Study as a Model for Success

The dual study model integrates academic education with continuous practical experience within the company. From the beginning, students were involved in real projects, enabling them to take on responsibilities in development and digitalization initiatives.

This collaboration enables:

- Early application of theoretical concepts in an industrial context
- Continuous knowledge and technology transfer
- Targeted skill development in accordance with specific product and process requirements
- Sustainable personnel development with high practical relevance

The close cooperation between Hochschule Niederrhein and Brain of Materials has significantly contributed to this success. The combination of a solid academic education and project-based implementation within the company creates an environment where innovation can grow systematically.

#### Continuity and Perspective

The end of their studies does not mark the conclusion of their collaboration - in fact, it signifies the opposite. All three graduates will continue their roles at Brain of Materials, ensuring that valuable know-how remains within the company and can be further developed.

Brain of Materials takes pride in supporting young talents on their academic and professional journeys. The successful development of the dual students underscores the company's commitment not only to actively shape technological standards but also to invest in the next generation of digital and AI experts.

 

#### Summary

The successful Bachelor’s degrees of the three dual students exemplify:

- The effectiveness of practical AI research
- The close, productive collaboration between academia and industry
- The sustainable development of qualified young professionals

Brain of Materials expresses gratitude for the commitment, professional excellence, and constructive collaboration and looks forward to jointly shaping the next steps in innovation in AI-driven material data processes.

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