项目介绍

2026年7月2日

Are you our new colleague? 

We seek a PhD student in responsible AI for mental health risk prediction, who is passionate about the technical detective work needed to uncover the strengths and weaknesses of AI algorithms.

AI becomes increasingly used for mental health purposes, ranging from triaging available healthcare resources, via diagnostic decision support, to AI agents used by patients for coaching or information retrieval. In this PhD project we put a particular emphasis on risk prediction models for major depressive disorder (MDD) and aim to design and stress-test such models in order to uncover their robustness and weaknesses.

As a PhD student on this project, you will develop MDD risk prediction models based on tabular registry data, and study their uncertainty, fairness, and transparency, as well as their robustness across populations and modelling choices.

In addition to creating and implementing risk prediction models, your research will also include developing and stress-testing methods for uncertainty quantification, explainable AI, algorithmic fairness and general performance audit, when applied to the mental health risk prediction models.

The position requires both a practical skill set in developing a range of machine learning models and responsible AI tools, but also a methodological skill set to be able to utilize and manipulate both probabilistic and explainable machine learning models in order to understand and demonstrate their strengths and weaknesses.

You will be part of an interdisciplinary team from the TRUSTMIND project (https://trustmindproject.github.io/) including machine learners, epidemiologists, and ethics and legal researchers, and your job will include both the technical development of predictive models, but also participation in interdisciplinary work that studies how and whether they can be used for good.

Responsibilities and qualifications
Your main tasks will be to implement a range of risk prediction models utilizing different data and with different levels of complexity; developing tools to audit them; and collaborating with the rest of the team to understand their strengths and weaknesses from technical, ethical and legal standpoints. 

Your primary tasks will be to:

• Implement mental health risk prediction models on registry data
• Implement common responsible AI tools that aim to highlight their strengths and weaknesses
• Develop methods that highlight the robustness or lack thereof in not only the prediction models, but also in the state-of-the-art responsible AI tools
• Develop improved auditing tools for monitoring uncertainty, transparency and bias in such risk prediction models.
• Collaborate with an interdisciplinary team of machine learners, epidemiologists, ethical researchers and legal researchers
• Writing and presenting scientific papers in top conferences and journals
• Working as a teaching assistant in four 5 ECTS modules

You must have a two-year master’s degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master’s degree.

Approval and Enrolment 
The scholarship for the PhD degree is subject to academic approval, and the candidate will be enrolled in one of the general degree programmes at DTU. For information about our enrolment requirements and the general planning of the PhD study programme, please see DTU’s rules for the PhD education. 

Assessment
The assessment of the applicants will be made by Aasa Feragen from DTU Compute, and Melanie Ganz and Sune Holm from the University of Copenhagen.

We offer
DTU is a leading technical university globally recognized for the excellence of its research, education, innovation and scientific advice. We offer a rewarding and challenging job in an international environment. We strive for academic excellence in an environment characterized by collegial respect and academic freedom tempered by responsibility.

Salary and appointment terms
The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations. The allowance will be agreed upon with the relevant union. 

The period of employment is 3 years. The starting date is 1 October 2026 or according to mutual agreement. The position is a full-time position.

You can read more about career paths at DTU here.

Further information
Further information may be obtained from Aasa Feragen, DTU Compute, afhar@dtu.dk. 

You can read more about DTU Compute at www.compute.dtu.dk.

If you are applying from abroad, you may find useful information on working in Denmark and at DTU at DTU – Moving to Denmark. Furthermore, you have the option of joining our monthly free seminar “PhD relocation to Denmark and startup “Zoom” seminar” for all questions regarding the practical matters of moving to Denmark and working as a PhD at DTU. 

Application procedure 
Your complete online application must be submitted no later than 25 July 2026 (23:59 Danish time). Applications must be submitted as one PDF file containing all materials to be given consideration. To apply, please open the link “Apply now”, fill out the online application form, and attach all your materials in English in one PDF file. The file must include:

• A letter motivating the application (cover letter)
• Curriculum vitae 
• Grade transcripts and BSc/MSc diploma (in English) including official description of grading scale

You may apply prior to ob­tai­ning your master’s degree but cannot begin before having received it.

Applications received after the deadline will not be considered.