PostDoc in Causal Machine Learning and Reinforcement Learning (3 years)

AcademicTransfer; Netherlands; On-site; EUR 41,000 - 64,000; Full time

Job summary

Vrije Universiteit Amsterdam invites applications for a PostDoc position focused on research at the intersection of machine learning and statistics. The role emphasizes causal inference and reinforcement learning, with applications to health data within two major projects.

The position is embedded in the Quantitative Data Analytics group and collaborates with Amsterdam UMC, Erasmus MC and Groningen university partners.

Qualifications

• PhD in machine learning and/or statistics.
• Publications showing ML health-domain applications preferred.
• Strong programming with PyTorch and/or TensorFlow.
• Ability to work independently and as part of a team.

Responsibilities

• Perform research in ML and statistics and publish in top AI/ML conferences.
• Collaborate with the QDA group and partner labs (Amsterdam UMC, Erasmus MC, Groningen).
• Contribute to Stress in Action and MINMED projects and interact with partners.
• Contribute to teaching activities and supervise Bachelor/Master students (≈20% time).

Skills

PhD in ML/Statistics

Programming in PyTorch and/or TensorTF

Education

PhD in ML or Statistics

Tools

PyTorch

TensorFlow

Job description

Organisation/Company Vrije Universiteit Amsterdam (VU) Research Field Mathematics » Statistics Researcher Profile Recognised Researcher (R2) Application Deadline 8 Sep 2026 - 16:18 (UTC) Country Netherlands Type of Contract Temporary Job Status Not Applicable Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

We are looking for a PostDoc who will do research on the intersection of machine learning (ML) and statistics. More specifically, the focus is on the development of algorithmic improvements in causal inference and reinforcement learning through the cross fertilization of machine learning and statistical approaches and applying these innovations in a health setting.
The position is embedded in two projects which act as application domains. First of all, the ten-year gravitation grant Stress in Action, funded through NWO (Dutch National Science Foundation). The goal of the project is to move stress research from the lab into daily life, and hence, the use cases of the methodological innovations will focus on applications to stress-related data, such as wearables, ecological momentary assessments, and other types of questionnaires. Secondly, the ZonMW funded MINMED project which aims to unlock causal effects of interventions in elderly care from observational data.
A variety of academic partners are involved in the two projects, including Vrije Universiteit Amsterdam, Amsterdam UMC, Erasmus MC, University of Twente, University of Groningen, UMC Groningen, and Utrecht University.
The position is embedded in the Quantitative Data Analytics (QDA) group of the VU in close collaboration with the Amsterdam UMC, Erasmus MC and the University of Groningen. The QDA group focuses on both fundamental and application-driven research in ML.
We are looking for candidates with experience in the area of machine learning or statistics, with a special preference for those with expertise in causal inference and/or reinforcement learning, ideally complemented with a solid background in statistics. We are interested in attracting a PostDoc who is able to perform ground-breaking research on fundamental aspects of machine learning and statistics and wants to apply these innovations in the context of the Stress in Action and MINMED projects.

Your duties

The PostDoc position is focused on performing research and contains the following duties:

• performing research in the field of machine learning and publishing in top AI and ML conferences
• participating in the QDA group of VU (lead by Mark Hoogendoorn), the Biostatistics group of Erasmus MC (led by Dimitris Rizopoulos), and Laura Bringmann's intensive longitudinal data lab at the University of Groningen.
• contributing to the Stress in Action and MINMED projects and interacting with the different partners and stakeholders in the project
• contributing to some teaching activities and possibly supervising Bachelor and Master students (20% of the time)

We will base our selection on the following components:

• a PhD degree in an area relevant to machine learning and/or statistics
• prior experience in machine learning, ideally also on applications in the health domain, demonstrated by relevant publications
• a strong record of computer programming, with experience in pytorch and/or tensorflow
• demonstrated ability to work both independently and as a team member

As a university, we strive for equal opportunities for all, recognising that diversity takes many forms. We believe that diversity in all its complexity is invaluable for the quality of our teaching, research and service. We are always looking for talent with diverse backgrounds and experiences. This also means that we are committed to creating an inclusive community so that we can use diversity as an asset.
We realise that each individual brings a unique set of skills, expertise and mindset. Therefore we are happy to invite anyone who recognises themselves in the profile to apply, even if you do not meet all the requirements.

Additional Information

A challenging position in a socially involved organization. The salary will be in accordance with university regulations for academic personnel and amounts € 3.706,00 per month during the first year and increases to € 5760,00 per month during the fourth year, based on a full-time employment. The job profile: (scale 10) is based on the university job ranking system and is vacant for 1 FTE. The appointment will initially be for a duration of 3 years at VU Amsterdam, including a 2 months probation.
Additionally, Vrije Universiteit Amsterdam offers excellent fringe benefits and various schemes and regulations to promote a good work/life balance, such as:

• a maximum of 41 days of annual leave based on full-time employment,
• 8% holiday allowance and 8.3% end-of-year bonus
• discount on (and occasionally exclusive access to) theater performances and courses at the Griffioen Cultural Center,
• a wide range of sports facilities which staff may use at a modest charge,
• contribution to commuting expenses