MSc or BSc Thesis / Internship (m/f/d) AI for Intent-Based Networking

You want to support industrial research projects and teams with your interest in network management and orchestration technologies, developing methods to automate network operation beyond office IT for industrial use-cases with state of the art networking technologies? Then join ABB, a global key player in industrial automation and smart electrification, as an intern or student worker. Apply your skills and grow personally in a challenging and fascinating environment. 

ABB is a technology leader driving the digital transformation of industries toward autonomous industrial plants and buildings. Industrial systems comprise automation systems, networks and devices, software tools and platforms, and control solutions. 

In the ABB Research Center Germany, we focus on innovating digital industries and smart and sustainable electrification systems. In our team, we contribute to these goals with connectivity, system integration, and applied security competencies.  Become a member of our team and be part of the projects and research teams and their endeavor of demonstrating great results in an industrial automation environment and by supporting with UX approaches as well as prototype user interfaces.

• In close collaboration with our research team, you will be developing a concept to automatically generate requirements for automation networks from textual specifications.

• Analyzing the problem and conceptualizing a solution concept: define a target information model to capture the requirements. Consider the different types of traffic and specifications, which might require different approaches to extract the needed information.

• Implementing the concept: build the conversion concept using existing frameworks. Create a user interface to show, modify, add and audit the extracted requirements.

• Verification: use real project data to test the implementation. Define and run an approach to assess the quality and coverage of the result, i.e. the validity of the requirements and potential gaps in the extraction process.