Thesis Work: Safe Remote Learning-Based Control for Mining Operations
5 days ago
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 LeadThis thesis aims to develop remote/safe learning-based control framework for the application of underground mining operations.
Traditional control strategies face significant challenges due to uncertain dynamics, high safety risks, remote operations and the need for autonomous operations in constrained and hazardous conditions. We aim to design and validate a learning-based controller that not only learns optimal strategies but also integrates safety constraints and uncertainties to prevent unsafe actions.
By incorporating risk-sensitive policies, and safe exploration techniques, reliability and operational safety will be guaranteed.
Details
- Period: 5 months in 2026 (January/February – June/July)
- Number of credits: 30 ECTS
- Number of students for this thesis work: 1-2
- Location: ABB Research Center in Västerås
- ABB may cover the accommodation in Västerås
Your role and responsibilities
Develop remote & risk-aware control algorithms considering safety constraints to ensure reliable and secure operations in uncertain and hazardous mining conditions and consider delays between controller and plant which deteriorates the control efficiency.
In this regard, the state of the art for remote control solutions with learning uncertainties and safety guarantees will be reviewed, the appropriate safety guaranteed control algorithm are designed and the performance of the solution will be evaluated via numerical simulations.
Approach
The work will address the following points:
- Problem formulation
- Prior art review
- Method & solution development
- Validation by simulation/experiments
- Strong background in machine learning, control, computer science, or similar disciplines
- Motivated to solve real-world problems using state-of-the-art methods
- Self-driven and solution oriented
- Good programming skills (Python/MATLAB)
More about us
Supervisor Maryam Sharifi, , and Alf Isaksson, , will answer all your questions about the thesis topic and expectations. Recruiting Manager Linus Thrybom, , will answer your questions regarding 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 to learn more about us and see the impact of our work across the globe.
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