Master's Thesis Worker - Beyond Human-in-the-Loop Learning
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Understanding Data Fluency in Self-Maintaining Data Ecosystems
Background
TRATON GROUP is one of the world’s leading commercial vehicle manufacturers, with brands including Scania, MAN, International and Volkswagen Truck & Bus. Its portfolio covers light commercial vehicles, trucks and buses, complemented by financing, charging and digital logistics services. Through its global operations, production sites and extensive sales and service networks, TRATON has access to diverse vehicle platforms, real-world operational data and fleet deployment environments. This provides a strong foundation for developing and validating innovative solutions for sustainable and efficient transportation.
We are a cross-functional enablement team within TRATON R&D, responsible for data platforms, AI strategies, and human-centered technology development. Our mission is to empower R&D engineers to use trustworthy data and smart tools to make faster, better decisions. You’ll work closely with experts in AI, product development, and data engineering– in a context where your research can directly shape the way we build the future of transportation.
Target Scope
What will Battery & Charging R&D look like as domain teams build strong data capabilities and data systems increasingly communicate with one another? Many current visions of AI-enabled work emphasise speed, efficiency and reduced human involvement. Less is known about how domain teams’ functions and skills may change as systems take on more responsibility, and how to develop systems that remain resilient as they become more connected and automated.
Objective
This thesis will explore a plausible five-to-ten-year horizon in Battery & Charging R&D. It will examine what resilience means for data systems and infrastructures that exchange information, monitor processes and adapt over time: how they can remain reliable as conditions change, how their dependencies affect one another, and how people can understand when systems need attention or intervention. The study will also consider how the roles, responsibilities and skills of domain experts may change as they work with and oversee these systems.
A current initiative such as Nest may provide a point of reference for understanding this transition. The focus, however, is on the future of B&C domain work and on developing resilience, not only on how engineers use AI tools. The research will consider how AI-enabled and data-enabled futures relate, and how different expectations about them shape plans for investment and capability development.
Potential Contribution
The thesis aims to help B&C develop a more grounded view of how to build resilient data systems and prepare resilient engineering teams as data systems become more connected and autonomous by incorporating strategic foresight pertaining to AI Futures. It will examine the socio-technical conditions for resilience: how systems can remain reliable and adaptable, and what domain expertise, oversight and skills are needed around them.
The findings could help B&C identify capabilities to develop, set milestones for connected and automated systems, and support decisions about the pace and direction of AI development in the area. This includes considering how systems should be designed and supported, what responsibilities domain teams will retain, and what skills engineers require to develop in order to understand system behaviour and intervene when required.
The expected output is an evidence-informed future scenario for a selected B&C context, together with a framework or set of considerations for system resilience, changing domain roles and future skill needs. This can give managers a basis for planning investments in both resilient data ecosystems and the engineering capabilities needed to work with them.
Job Description
Conduct a pre-study of research and organisational expectations concerning AI-enabled R&D, data ecosystems, system resilience and future engineering work.
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Select a relevant research context within Battery & Charging in dialogue with supervisors and stakeholders.
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Design and conduct a mixed-method study with relevant domain experts and other stakeholders.
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Explore how domain teams' responsibilities, decision-making and oversight may change as data systems become more connected and automated.
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Examine what resilience means for the selected data systems and infrastructures, including how they remain reliable and adaptable as conditions change and how people can recognise when attention or intervention is needed.
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Investigate the capabilities and skills engineers may need to understand, assess and work with these systems.
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Develop an evidence-informed future scenario, framework or early-stage indicators to support discussion of system resilience, future capabilities and investment milestones.
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Translate the findings into recommendations for resilient data ecosystems, B&C capability development and AI investment planning for analytics enablement.
Who You Are
You are interested in how data, AI, and automation are implemented and used in cyber-physical systems, particularly when these systems operate with less continuous human intervention. You are curious about how such changes affect learning, agency, responsibility, and decision-making in engineering organisations.
Ideally, you have:
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An educational background in human–computer interaction, cognitive science, industrial engineering, autonomous systems, data science, computer science, or a related field.
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Previous experience in mixed-methods research and creative research approaches, such as speculative design, provocative design, or practice-based inquiry.
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Experience from research, academic, or professional projects involving digitalisation, data-driven practices, AI, or other emerging technologies.
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An interest in one or more of the following areas: digitalisation, human–AI interaction, more-than-human design, datafication, or socio-technical systems.
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Ability to engage critically with complex and emerging technologies, including their social, organisational, and ethical implications.
Application
Contact persons and supervisor
If you have questions or would like further information, please contact:
Manager:
Sarah Hantosi Albertsson
Head of Global Analytics Enablement
Supervisor:
Eva Banerjee
Service Designer and Industrial Doctoral Student in Human-Computer Interaction
This research work is undertaken in collaboration with researchers at KTH
We look forward to your application!
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