项目介绍

2026年4月9日

Are algorithms neutral tools, or do they actively shape the world they model? In this PhD, you will bridge the gap between building and critically studying Human-Centred AI systems. You will combine technical work on machine learning with qualitative analysis of how AI systems are interpreted and used in organisational decision-making. Join the Human-Centred Computing group to investigate how such systems are designed and how they shape real-world decisions.

Your job

AI systems increasingly inform decision-making across organisational contexts, yet their representational role remains underexplored. Rather than functioning as neutral tools, algorithmic models translate complex realities into scores, rankings, classifications, and predictions. In doing so, they shape how problems are defined, which options become visible, and how agency is distributed between humans and algorithmic systems.

In this PhD project, you will study AI-supported decision-making from a representational perspective. You will investigate how algorithmic models are designed, how they come to stand in for aspects of the world, and how they are interpreted, contested, and revised in practice. Decision-making is approached as a situated process emerging through the interaction of data, models, professional judgement, and organisational context.

Depending on your interests and empirical setting, the project will involve collaboration with external partners working on applied AI systems. Possible domains include geospatial analytics (for example in collaboration with a startup), as well as decision-support applications in legal and public-sector contexts such as law enforcement or the judiciary. These settings provide opportunities to study how algorithmic representations interact with institutional norms, evidentiary standards, and professional expertise.

Against this background, several research avenues may be explored, depending on your interests and the empirical settings of the project. These may include work on explainable AI (XAI), approached as a socio-technical rather than purely technical challenge, examining how explanations function in practice, how they relate to discretion and accountability, and how AI systems can remain responsive to contextual knowledge in real decision environments. Another possible avenue concerns theorising with data, focusing on how inductive machine learning, deductive modelling, and abductive interpretation interact in practice, and how AI systems participate in cycles of sense-making within organisations. In addition, the project may engage with the role of spatial representations and embeddings, exploring how spatial abstractions and learned representations structure similarity, proximity, and relevance in decision-making processes.

The PhD will be conducted in collaboration with external organisations, providing access to real-world datasets and decision contexts. Throughout the project, you will be encouraged to publish in interdisciplinary venues, contribute to educational activities, and develop as a researcher who is comfortable operating at the intersection of computational modelling, organisational decision-making, and Human-Centred Computing.

Your qualities

We are excited to welcome an enthusiastic new colleague who thrives in an interdisciplinary and international team and meets the following criteria:

• A recently completed (or nearly completed) Master’s degree in a socio-technical programme, such as: (Applied) Data Science, Econometrics, Artificial Intelligence, (Computational) Social Science, Human Computer Interaction.
• Experience with at least one of the following, and a willingness to learn the other:
      – Quantitative research (e.g., user studies, statistics, machine learning)
      – Qualitative research methods (e.g., interviews, case studies, interpretive analysis)
• Affinity with real-world applications and openness to working with public or private sector organisations.
• Demonstrable programming skills, for example, experience in Python or R.
• Proficiency in English, both spoken and written (C1 level). Proficiency in Dutch is considered an advantage due to collaboration with Dutch stakeholders.

Our offer

• a position for 18 months, with an extension to a total of four years upon successful assessment in the first 18 months;
• a gross monthly salary starting at € 3,059 in the first year and increasing annually up to €3,881 in the fourth year, based on full-time employment i (salary scale P under the Collective Labour Agreement for Dutch Universities (CAO NU));
• 8% holiday pay and 8.3% year-end bonus;
• a pension scheme, partially paid parental leave and flexible terms of employment based on the CAO NU.

In addition to the terms of employment laid down in the CAO NU, Utrecht University also offers a range of its own schemes for employees. This includes arrangements for professional development, various types of leave, and options for sports and cultural activities. You can also tailor your employment conditions through our Terms of Employment Options Model. In this way, we encourage you to keep investing in your personal and professional development. For more information, please visit Working at Utrecht University.

About us

A better future for everyone. This ambition motivates our scientists in executing their leading research and inspiring teaching. At Utrecht University, the various disciplines collaborate intensively towards major strategic themes. Our focus is on Dynamics of Youth, Institutions for Open Societies, Life Sciences and Pathways to Sustainability. Sharing science, shaping tomorrow.

Working at the Faculty of Science external link means bringing together inspiring people across disciplines and with a variety of perspectives and backgrounds. The Faculty has six departments: Biology, Pharmaceutical Sciences, Information & Computing Sciences, Physics, Chemistry and Mathematics. Together, we work on excellent research and inspiring education. We do so, driven by curiosity and supported by outstanding infrastructure. Visit us on LinkedIn and discover how you can become part of our community.

You will join the Human-Centred Computing group external link of the Interaction division. The Human Centred Computing group studies how people use computerised systems in their everyday lives and designs novel interaction technologies for intelligent systems. Our research areas include, amongst others, personalised and adaptive systems, user modelling and recommender systems, human interaction with embodied AI, and decision support systems. 

More information

For more information, please contact Daan Kolkman external link at d.a.kolkman@uu.nl. 

Do you have a question about the application procedure? Please send an email to science.recruitment@uu.nl.

Candidates for this vacancy will be recruited by Utrecht University.

Apply now

As Utrecht University, we want to be a home for everyone. We value staff with diverse backgrounds, perspectives and identities, including cultural, religious or ethnic background, gender, sexual orientation, disability or age. We strive to create a safe and inclusive environment in which everyone can flourish and contribute.

Knowledge security screening can be part of the selection procedures of academic staff. We do this, among other things, to prevent the unwanted transfer of sensitive knowledge and technology.

If you are enthusiastic about this position, just apply via the “Apply now” button! Please enclose:

• your letter of motivation; Your letter of motivation (max. 1 page), in which you explain your interest in the position and clearly demonstrate how your previous experience, skills, and qualifications align with the requirements outlined under ‘Your qualities’ and with the aims of the research project.
• your Curriculum Vitae;
• scans of BSc and/or MSc certificates (incl. grade list), with translation if not in Dutch or English;
• the names, telephone numbers, and email addresses of at least two references;
• if you have not completed your Master yet, please provide a letter of your thesis supervisor / master coordinator indicating your expected completion date.