Leiden University
Closing soon
1 week ago
PhD in Machine Learning for Scientific Inference in Behavioural Science Leiden University in Netherlands
Degree Level
PhD
Field of study
Computer Science
Funding
The position is fully funded by the Dutch Research Council (NWO) with a competitive MSCA-style PhD salary (€3059–€3881 gross per month), holiday allowance (8%), year-end bonus (8.3%), ABP pension, full reimbursement of commuting costs, flexible working hours, hybrid options, home-working allowance, laptop, mobile phone, and opportunities for personal development.
Deadline
Mar 13, 2026
Country
Netherlands
University
Leiden University

How do Indian students apply for this?
Sign in for free to reveal details, requirements, and source links.
Where to contact
Keywords
About this position
Leiden University’s Faculty of Social and Behavioural Sciences, Institute of Psychology, Methodology and Statistics Unit, is offering a fully funded PhD position in Machine Learning for Scientific Inference in Behavioural Science. The project, funded by the Dutch Research Council (NWO), aims to make machine learning scientifically useful for behavioural science by developing interpretable machine learning methods, uncertainty quantification, and ML-based meta-analysis to combine and compare results across studies.
The successful candidate will develop statistical methods and open-source software, test these methods through simulations and real-world datasets, collaborate with behavioural science researchers, publish results, and present at international conferences. The position offers a supportive and inclusive research environment, with opportunities for personal development, tailored courses, and workshops.
Applicants must hold a Master’s degree in statistics, data science, psychology, or a related quantitative field. Required skills include strong programming in R, experience with data analysis and Monte Carlo simulations, and excellent English communication. A genuine interest in behavioural science is essential. Desirable qualifications include experience in Bayesian high-dimensional regression, interpretable machine learning, meta-analysis, and statistical methods or software development.
The position is full-time (38 hours/week), initially for one year with the possibility of extension up to four years. The salary ranges from €3059 to €3881 gross per month, with additional benefits such as an 8% holiday allowance, 8.3% year-end bonus, ABP pension, full reimbursement of commuting costs, flexible working hours, hybrid options, home-working allowance, laptop, and mobile phone.
To apply, submit your CV and motivation letter by 13 March 2026. For more information, contact Dr. Marjolein Fokkema at [email protected]. Interviews will be held between 14–24 March 2026. For full details and to apply, visit the official Leiden University careers portal.
Funding details
The position is fully funded by the Dutch Research Council (NWO) with a competitive MSCA-style PhD salary (€3059–€3881 gross per month), holiday allowance (8%), year-end bonus (8.3%), ABP pension, full reimbursement of commuting costs, flexible working hours, hybrid options, home-working allowance, laptop, mobile phone, and opportunities for personal development.
What's required
Applicants must have a Master’s degree in statistics, data science, psychology, or a related quantitative field. Strong programming skills in R, experience with data analysis and Monte Carlo simulations, and strong English communication skills are required. A genuine interest in behavioural science is essential. Desirable qualifications include experience in Bayesian high-dimensional regression, interpretable machine learning, meta-analysis, and statistical methods or software development.
How to apply
Submit your CV and motivation letter by 13 March 2026. Contact Dr. Marjolein Fokkema at [email protected] for inquiries. Interviews will be held between 14–24 March 2026. Apply via the official Leiden University careers portal.
Ask ApplyKite AI

How do Indian students apply for this?
Sign in for free to reveal details, requirements, and source links.