PhD student to join our cutting-edge project TRUSTMIND
Role highlights
Full Time
Permanent
Entry
On-site
This PhD position requires a strong foundation in AI, machine learning, and statistical modeling, with a focus on developing trustworthy AI algorithms for mental health applications, specifically major depressive disorder (MDD) risk prediction. Candidates should hold a master's degree in Computer Science, Data Science, Statistics, or a related field, and demonstrate excellent programming skills in Python and R, with practical experience building machine learning models in Python. The role emphasizes research into algorithmic fairness, transparency, and bias mitigation within AI diagnostics, necessitating knowledge of explainable AI (XAI) methods and demographic or cultural considerations in mental health data. Collaboration with interdisciplinary teams, including mental health experts, is essential for clinically relevant validation and advancing ethical AI solutions. Strong communication skills for publishing research and presenting findings are required, alongside the ability to work both independently and collaboratively. Experience in healthcare AI, mental health research, or biostatistics is advantageous, as is knowledge of the Danish language. The position offers an opportunity to engage in cutting-edge, societally impactful research at the University of Copenhagen's Department of Computer Science, within a full-time, 36-month funded project. Overall, the ideal candidate combines technical expertise in AI and statistics with a commitment to ethical, transparent algorithm development and interdisciplinary collaboration in mental health contexts.
About the role
Role Summary
Join the TRUSTMIND project, led by Melanie Ganz, focused on enhancing the reliability of trustworthy AI methods in mental health risk prediction, particularly for major depressive disorder (MDD). The project aims to develop ethical, transparent, and clinically informed AI algorithms for MDD screening using diverse mental health datasets.
Key Objectives
- Develop new MDD risk scoring algorithms to address challenges in AI-driven mental health diagnostics
- Develop methods to estimate and mitigate bias in AI diagnostics
- Collaborate with mental health experts to ensure clinically relevant validation schemes
Responsibilities
- Conduct research on algorithmic fairness and transparency in AI
- Analyze demographic and cultural dependencies in mental health diagnostics
- Develop and implement methods to mitigate bias in AI algorithms
- Collaborate with interdisciplinary teams, including AI and mental health experts
- Publish research findings in high-impact journals and present at conferences
- Contribute to advancing trustworthy AI in mental health
Requirements
- MSc in Computer Science, Data Science, Statistics, or related field
- Strong background in AI, machine learning, and statistical modeling
- Excellent programming skills (e.g., Python, R), with practical experience in machine learning models in Python
- Ambitious and motivated to make a societal impact with AI
- Excellent verbal and written communication skills
- Ability to work independently and collaboratively
Nice to Have
- Experience with explainable AI (XAI) and algorithmic fairness
- Experience in healthcare AI or mental health research
- Knowledge of biostatistics or Danish language
Benefits
- Work at the Department of Computer Science (DIKU), University of Copenhagen, a leading European institution
- Engage in cutting-edge research within a collaborative and interdisciplinary environment
- Full-time position (37 hours/week)
- Salary and terms according to the agreement between Danish Universities and The Danish Confederation of Professional Associations
Duration
- 36 months, starting 1 April 2026 or as soon as possible thereafter by mutual agreement
How to Apply
- Application deadline: 4 January 2026, 23.59 CET
- Applications received after the deadline will not be considered
- For further information, contact Associate Professor Melanie Ganz at [email protected]
- Please mention in your application that you found the job at Jobindex
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