PhD Position in Generative AI in Health Technology Assessment (HTA)

How can generative AI (GenAI) support Health Technology Assessment (HTA) of medicines? HTA bodies and technology developers are already experimenting with GenAI, but systematic evidence on where it adds value and where its risks lie, is lacking. In this PhD project you will map current and future GenAI use across the full HTA lifecycle, from horizon scanning to reassessment, and translate these insights into concrete, evaluated tools for HTA assessors.

Your job

You will be on the frontline of using and implementing GenAI in the cyclic HTA approach to medicines. Possible work topics include:

• Mapping current and anticipated use of GenAI across the HTA lifecycle of medicines. This may include horizon scanning and early dialogue through initial assessment, real-world evidence generation and reassessment. You will focus on how international HTA bodies anticipate it will shape their activities in the coming years.
• Assessing the extent to which GenAI tools can reliably extract, harmonise, and analyse information from national and EU HTA reports, and identifying the conditions required to scale this across jurisdictions.
• Developing how GenAI can may link the methodological needs of HTA bodies to existing and newly developed methods, in a system that is useful in practice.
• Examining how health technology developers are using GenAI in the preparation of national and EU HTA submissions, and what this implies for how HTA bodies assess GenAI-supported submissions.

To study these questions, you will use a variety of methods, including comparative document analyses, quantitative descriptive and association studies, complex systems analyses, automated extraction data analysis and methods-needs matching

Your PhD position is part of the Academic Research Network HTA, a collaboration between the National Health Care Institute (Zorginstituut Nederland (ZIN)), Erasmus University Rotterdam, and Utrecht University (UU). The Network aims to bridge HTA research and policy by supporting specific HTA research projects closely linked to policy questions. You will work closely with other PhDs in this network and interact regularly with the HTA policy officers at ZIN. Your work will also involve close collaboration with academic experts from UU and (inter)national experts in generative AI in healthcare. Finally, you will be expected to contribute to HTA teaching for bachelor and master students.

Your qualities

We are looking for a candidate who meets the following criteria:

• an MSc in pharmaceutical or (bio)medical sciences, health economics and/or HTA, epidemiology, data sciences or another relevant field;
• the ability and enthusiasm to learn and apply quantitative and qualitative methods within HTA;
• an interest in cross-disciplinary work within HTA;
• good oral and written communication skills in English are essential; knowledge of Dutch is considered a big advantage, particularly for communication with Dutch HTA and policy stakeholders;
• the ability to work independently, reflect critically on your work, and take initiative and responsibility for your projects.

We consider it a plus if you have experience with:

• data analyses: qualitative (e.g. NVivo) and quantitative (e.g. R, SAS);
• using GenAI, for instance Large Language Models (LLMs) for the extraction and analysis of data as part of health research.

Our offer

• a position for 18 months, with an extension to a total of four years upon a positive assessment in the first 18 months;
• a gross monthly salary ranging from €3,204 in the first year to €4,051 in the fourth year in the case of full-time employment (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 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 be employed by the Department of Pharmaceutical Sciences at Utrecht University, within the Utrecht Institute for Pharmaceutical Sciences (UIPS), the Department’s research institute. The position is based in the Division of Pharmacoepidemiology and Clinical Pharmacology and embedded in the Centre for Pharmaceutical Policy and Regulation, a multidisciplinary, internationally oriented research environment focusing on pharmaceutical policy, drug regulatory science, and health technology assessment (HTA).

More information

For more information, please contact: Dr. C. Leopold at c.leopold@uu.nl.

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

Apply now

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• your letter of motivation;
• your Curriculum vitae;
• the names, telephone numbers, and email addresses of at least two references;

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