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Thesis Work for AI-Assisted Design of Plantwide Control Architectures: An Application Study in the Process Industry

ABB Inc.
United States, North Carolina, Cary
305 Gregson Drive (Show on map)
Nov 29, 2025
At ABB, we help industries outrun - leaner and cleaner. Here, progress is an expectation - for you, your team, and the world. As a global market leader, we'll give you what you need to make it happen. It won't always be easy, growing takes grit. But at ABB, you'll never run alone. Run what runs the world. This Position reports to: R&D Team Lead In modern process plants, overall control performance depends not only on how individual controllers are tuned but on how they are structured and interconnected across the plant. Designing this plantwide control architecture - defining control loops, interactions, and coordination strategies - remains a knowledge-intensive task that relies heavily on experienced control engineers. This thesis explores how artificial intelligence and machine learning can be leveraged to support plantwide control architecture design. The goal is to develop a proof-of-concept tool that interprets process information and engineering documentation to recommend suitable control configurations and structures. By assisting engineers in the early design phase, the tool aims to improve consistency, accelerate project execution, and enhance knowledge reuse in process automation. Details: Start: January/February 2026 , duration of 5 months 30 ECTS per student 1-2 students Location: On-site at ABB Corporate Research in Vasteras (ABB may cover the accommodation) Your role and responsibilities Research questions: AI-Assisted Selection of Control Structures in Plantwide Automation Ontology and Knowledge Graph Development for Process Automation AI/ML for Control Strategy Classification and Recommendation Goals: Develop an AI-Based Framework for Control Architecture Recommendation Demonstrate and Evaluate the Framework in an Industrial Context Approach - the work will address the following points: Literature review on control structures and AI applications in process control Develop a prototype tool using existing LLM APIs (ABB AI) Implement basic P&ID interpretation (text extraction and pattern recognition) Create prompt engineering strategies for control structure recommendation Validate the tool with real test cases Qualifications for the role Drive and interest in working with real-world use cases A background in control engineering or computer science/AI is suitable. We will provide support to help you build the required skills and knowledge during the project, especially in the areas of Machine learning, Large Language Models, Process control Programming environment: Python and MATLAB Self-driven and solution-oriented Good spoken and written English Ability to analyze results and draw conclusions Good soft skills and collaborative spirit More about us Supervisor Soroush Rastegarpour, soroush.rastegarpour@se.abb.com, will answer all your questions about the thesis topic and expectations. Recruiting Manager Linus Thrybom, +46 730 809 906, will answer your questions regarding the hiring. Positions are filled continuously. Please apply with your CV, academic transcripts, and a cover letter in English. We look forward to receiving your application! A Future Opportunity Please note that this position is part of our talent pipeline and not an active job opening at this time. By applying, you express your interest in future career opportunities with ABB. We value people from different backgrounds. Could this be your story? Apply today or visit www.abb.com to learn more about us and see the impact of our work across the globe.