Thesis Work

W ABB pomagamy przemysłowi wyprzedzać konkurencję - działać sprawniej i ekologicznie. U nas postęp to standard – dla Ciebie, Twojego zespołu i całego świata. Jako globalny lider dajemy Ci to, czego potrzebujesz, by to osiągnąć. Nie zawsze będzie łatwo, rozwój wymaga determinacji. Ale w ABB nigdy nie będziesz działać sam. Zarządzaj tym, co porusza świat.

Stanowisko to podlega:

R&D Team Lead

Rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) has shown significant potential to support proactive decision-making, improve operational efficiency, and manage complex, time-dependent processes in industrial settings. However, their adoption in high-stakes industrial domains remains challenged by issues such as trust, interpretability, and the handling of uncertainty, especially when decisions carry significant risk.

In industrial settings, AI will eventually be positioned so that operators can rely on AI to make decisions. Yet, when these decisions are critical and outputs are uncertain or ambiguous, how workers respond, and how systems can support them, remain unanswered questions.

This thesis investigates how AI systems and humans can collaborate when AI outputs are uncertain, and how that affects decision-making. Additionally, the thesis explores how Human-Machine Interfaces (HMIs) can be designed to better support users in interpreting and acting on such uncertainty when there is risk involved.

This thesis aims to delve into this topic, exploring questions such as:

• How do industrial workers respond to uncertain AI outputs?
• How can we design interactions that help users interpret and act on uncertain AI outputs?
• Explore what factors (such as risk) has an impact on operator decisions
• Explore what information is needed and how it could be communicated in critical situations to enable more confident decision support?

Both Human-AI interaction (HAI) and Explainable AI (XAI) are interdisciplinary fields, exploring how humans and AI systems communicate and collaborate in a clear, understandable and transparent manner. With a solid foundation in the industrial domain, this thesis aims to make a valid contribution to the area, specifically exploring - through user research and design prototypes, potentially design principles, UX patterns - how uncertainty can be managed to improve the decision support capabilities of AI systems.

Details

• Period: Spring 2026, typically between January - June
• Number of credits: 30 ECTS
• Number of students: 1 - 2
• Location: Västerås, Sweden. Possibility for remote work

Your role and responsibilities

• Conduct a literature review on uncertainty in AI and decision-making in industrial contexts
• Collaborate with domain experts to define user scenarios
• Design and prototype HMI concepts that explain and visualize uncertainty for end users without machine learning knowledge
• Plan and conduct user studies or expert evaluations to validate design concepts
• Document findings and contribute to knowledge on trustworthy AI systems

Your background

We are looking for motivated master's students with a strong interest in human-centered design and automation.

Candidates could have:

• Academic Background in one or more of the following fields:
• Human-Computer Interaction (HCI)
• Interaction Design
• Human Factors Engineering
• Cognitive Psychology or Behavioral Science
• Systems Design or Ergonomics
• Socio-Technical Systems
• Interest in Human-Automation Collaboration, especially in autonomous industrial contexts (e.g., energy, maritime, manufacturing)
• Analytical Thinking, with the ability to interpret complex operational scenarios and translate them into design requirements
• Strong Communication Skills, both in Swedish and English, to collaborate with cross-functional teams and present findings clearly
• Self-Driven and Curious, with a passion for solving real-world challenges in industrial automation and remote operations

More about us

ABB is a global technology leader in electrification and automation. We see our purpose as being to enable a more sustainable and resource-efficient future. By connecting our engineering and digitalization expertise, we help industries run at high performance, while becoming more efficient, productive and sustainable so they outperform. We call this: 'Engineered to Outrun.'

Recruiting Manager Dawid Ziobro, , will answer your questions.

Apply with your CV, academic transcripts and a cover letter in English.

We look forward to receiving your application

Join us. Be part of the team where progress happens, industries transform, and your work shapes the world. Run What Runs the World.

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