Two PhD Positions in AI for Science at the University of Amsterdam

Job description
Curious how foundation models can accelerate environmental science, and how they reach their answers?
Join Us!
Foundation models are changing how science is done. To truly help scientists, they must work with real scientific data, and we must understand how they reach their answers. That is the challenge you will take on.
We are looking for two PhD candidates (four years each) to make AI both useful and understandable for science. You will join a collaborative team at the University of Amsterdam, led by Prof. Evangelos Kanoulas and Dr. Ana Lucic, and work closely with AI researchers and environmental scientists across Europe in the Horizon Europe project EVELYN. Together, we will turn cutting-edge AI into tools that help researchers understand and reduce environmental pollution.
This is what you will do
Position 1: Fine-tuning and retrieval for multimodal foundation models in science
Foundation models change how science is done: one model, pre-trained on broad data, can be adapted to many specialised tasks. Scientific tasks rarely look like a chat, though. Scientists need detection maps from satellite imagery, fast approximations of expensive simulations, gap-filled sensor records and ranked candidate explanations, each backed by evidence.
In this position you develop and evaluate methods that adapt multimodal foundation models to such tasks and ground their predictions in retrieved data and knowledge. You work with real EVELYN use cases (air quality, PFAS, microplastics, soil and water contamination, satellite detection of oil spills and plastic) and with the environmental scientists who will use the models. You will be part of the Information Retrieval Lab (IRLab) at the Informatics Institute.
What are you going to do?
We are looking for an enthusiastic and creative individual who is interested in:
Developing parameter-efficient methods that adapt foundation models to scientific tasks with diverse outputs, from segmentation maps to simulation fields and time series.
Developing automatic methods that decide where and how to adapt a model within a compute budget, for image, atmospheric and time-series models.
Developing retrieval-augmented models that use past observations, simulations and scientific literature to improve and justify their predictions.
Designing evaluation frameworks that compare foundation models fairly against strong specialist baselines and test whether outputs are supported by evidence.
Working with environmental scientists to turn their questions into machine learning tasks.
Publishing and presenting your findings at international AI conferences such as ICLR, SIGIR, ACL and EMNLP.
The exact topics and the work plan are defined together with the selected candidate.
Position 2: Mechanistic interpretability of machine learning models
As machine learning (ML) models gain influence on everyday life, it becomes increasingly important to understand how they work internally. Mechanistic interpretability is an emerging subfield of ML that reverse-engineers how modern deep learning architectures make predictions. In this position you focus on developing and evaluating post-hoc interpretability techniques, which explain predictions of models that have already been trained. You will be part of the Institute for Logic, Language and Computation (ILLC) and the Informatics Institute.
What are you going to do?
You conduct independent research on mechanistic interpretability. The field is young, so there is substantial room to contribute. The work can address various types of AI models (e.g., transformers, GNNs) and various applications, from social network analysis to molecular simulation: as long as the data contains ground-truth explanations, we can evaluate the methods we propose. We are looking for an enthusiastic and creative individual who is interested in:
Developing novel techniques for understanding how information flows through deep neural networks.
Developing evaluation frameworks for assessing the correctness of mechanistic interpretability techniques.
Connecting empirical findings about model behaviour to theoretical frameworks about computation.
Contributing to making AI systems more interpretable, reliable and safe.
Publishing and presenting your findings at international AI conferences such as NeurIPS, ICML, ICLR, FAccT, AAAI, ACL and EMNLP.
The exact topics and the work plan are defined together with the selected candidate.
What we ask of you
For both positions:
MSc in artificial intelligence, computer science, engineering, mathematics, physics, or a related discipline
Demonstrable background in machine learning
Excellent software engineering skills in Python
Fluent in English, both written and spoken
This is what we offer you
A temporary contract for 38 hours per week for the duration of 4 years (the initial contract will be for a period of 18 months and after satisfactory evaluation it will be extended for a total duration of 4 years). The preferred starting date is as soon as possible. This should lead to a dissertation (PhD thesis). We will draft an educational plan that includes attendance of courses and (international) meetings. We also expect you to assist in teaching undergraduates and master students.
The gross monthly salary, based on 38 hours per week and dependent on relevant experience, ranges between € 3,204 to € 4,051 (scale P). This does not include 8% holiday allowance and 8,3% year-end allowance. The UFO profile PhD Candidate is applicable. A favourable tax agreement, the ‘30% ruling’, may apply to non-Dutch applicants. The
Collective Labour Agreement of Universities of the Netherlands
is applicable.
Curious about our extensive secondary benefits package?
Read about all our benefits.
You will work in this team
The
Faculty of Science
has a student body of around 8,000, as well as 1,800 members of staff working in education, research or support services. Researchers and students at the Faculty of Science are fascinated by every aspect of how the world works, be it elementary particles, the birth of the universe or the functioning of the brain.
The
Informatics Institute (IvI)
of the Faculty of Science performs curiosity-driven and use-inspired fundamental research in five themes: Artificial Intelligence; Computational Science; Data Science; People, Society & Technology; and Systems & Networking. IvI is home to the Amsterdam unit of
ELLIS
, together with VU Amsterdam and CWI, and to
ICAI
, the Dutch network of AI labs that connects knowledge institutes with industry, government and societal partners.
Position 1 is embedded in the
Information Retrieval Lab (IRLab)
, which
works on search engines, recommender systems and conversational assistants, with data-driven methods across text, images and structured data. It was founded in 2020 out of the ILPS group and has researchers from over 15 countries. It takes part in ICAI labs (AIRLab Amsterdam, Elsevier Discovery Lab, Mercury Machine Learning Lab, Police AI Lab) and had 33 contributions at SIGIR and ICTIR 2026.
Position 2 is embedded in
Socially Intelligent Artificial Systems (SIAS)
group, which works on civic-centred, community-minded AI that aims to reduce inequality. With VU Amsterdam, the City of Amsterdam and the Ministry of the Interior, it runs the Civic AI Lab, part of ICAI.
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