Assistant Professor in AI for Science (AI4Science)

Are you passionate about advancing Machine Learning by integrating insights from the natural sciences Are you eager to bridge the 3rd (computational) and 4th (data-driven) paradigms of science by developing new AI approaches that discover fundamental principles through integrating knowledge from physics chemistry or biology We are looking for a creative researcher who views the laws of physics not as constraints but as the ultimate inductive bias for the next generation of AI foundation models.

We invite you to help shape the future ofAI for Science (AI4Science)as anAssistant Professor (UD)within theInformatics Institute. In this role you are expected to make significant contributions to world-class research and top-quality academic teaching developing your own independent research line while strongly contributing to the research profile of the Amsterdam Machine Learning Lab (AMLab).

AI for Science is maturing into a foundational discipline that accelerates scientific progress and enables breakthrough studies across domains. At AMLab we see this as a deeply synergistic endeavor:foundational AI research on methods that can accelerate scientific discovery where domain insights from the natural sciences drive the development of better AI.We seek research that fundamentally changes the way natural science is done by integrating domain knowledge into fundamental AI approaches rather than treating ML and the sciences as separate concerns. The most impactful work in this space does not merely apply existing AI to scientific data nor does it only use scientific data to benchmark ML models. Instead it tightly integrates the two developing new AI methodologies that are deeply informed by scientific structure and that in turn unlock new scientific understanding.

You will become a key PI within the Amsterdam Machine Learning Lab (AMLab) a world-renowned group at the forefront of AI research. You will collaborate broadly with researchers across the Faculty of Science including experimental groups in Chemistry Physics and Biology building closed-loop collaborations where scientific challenges inspire new ML methods and where those methods in turn enable new discoveries.

We invite a wide range of candidates to apply. We are broadly interested in foundational work on generative AI scalable architectures for scientific prediction tasks and other approaches that tightly couple ML methodology with scientific insight. To give a sense of the breadth of profiles we welcome examples of relevant research directions include (but are not limited to): understanding and solving PDEs for scientific computing using machine learning agentic AI for autonomous discovery (e.g. laying the computational groundwork for future self-driving labs) cross-domain multimodal scientific foundation models AI for formal verification and symbolic regression physics-inspired and geometric deep learning or simulation-based inference. We welcome your unique perspective and are eager to learn how your track record educational vision and future research goals align with the mission of AI for Science at AMLab.

The position entails a dedicated balance of 70% research and 30% teaching. You are expected to:Foster impactful research in AI4Science collaboratively developing your own research line and publishing in leading machine learning conferences (e.g. NeurIPS ICLR ICML) and scientific journals;

• Actively build bridges with experimental groups within the faculty (e.g. developing foundation models for control or paving the way for autonomous discovery setups) discovering new ML methodologies through these interdisciplinary closed-loop collaborations;
• Secure independent and
• collaborative funding from national (e.g. NWO) and European (e.g. ERC Horizon Europe) sources as well as industry partnerships;
• Supervise and mentor PhD candidates postdocs and MSc students fostering an inclusive and stimulating research environment;
• Take a leading role in shaping the AI education of the institute. You will teach machine learning courses at both the Bachelor and Master levels (e.g. in the Artificial Intelligence and Informatics programs);
• Actively contribute to the vibrant AI4Science ecosystem in Amsterdam championing verifiable and responsible AI for Science promoting the societal relevance of your research and playing an active role in the management and organizational tasks of the institute.

Your experience and profile:

• A PhD in Artificial Intelligence Machine Learning Computer Science or a closely related field;
• A strong track record in foundational Machine Learning research that is informed by or directly advances the natural sciences evidenced by publications in leading ML venues (e.g. NeurIPS ICLR ICML);
• A bilingual profile: the ability to integrate ML theory and domain science into a coherent research program rather than treating them as separate endeavors;
• A creative perspective on how scientific structure and domain knowledge can drive the development of new better AI methodologies (e.g. through inductive biases symbolic reasoning or multimodal scientific data);
• Demonstrated teaching abilities with the capacity to teach core Machine Learning courses at both the undergraduate and graduate levels;
• A solid track record (or excellent potential appropriate to your career stage) in acquiring external research funding;
• Excellent communication and collaboration skills to effectively bridge the gap between pure ML researchers and experimental scientists;
• Demonstrable organizational talent and leadership capabilities;
• Willingness to obtain a University Teaching Qualification (Dutch: BKO) within three years and motivation to learn the Dutch language within five years (supported by the institute with dedicated time and budget).

We offer a temporary employment contract for 38 hours per week for a period of 18 months. The preferred starting date is as soon as possible but can be discussed. A permanent contract follows if we assess your performance positive.

The gross monthly salary based on 38 hours per week and dependent on relevant experience ranges between 4728 to 6433 (scale 11). This does not include 8% holiday allowance and 83% year-end allowance. The UFO profile UD 2 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.

Starting conditions can be negotiated at the time of offer.

TheFaculty of Science has a student body of around 8000 as well as 1800 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 Amsterdam Machine Learning Lab (AMLab) conducts research in machine learning artificial intelligence and its applications to large scale data domains in science and industry. This includes the development of deep generative models methods for approximate inference probabilistic programming Bayesian deep learning causal inference reinforcement learning graph neural networks and geometric deep learning.

Want to know more about our organisation Read more aboutworking at the University of Amsterdam.

If you feel the profile fits you and you are interested in the job we look forward to receiving your application. You can apply online via the button. We accept applications until and including 2 June 2026.

Applications should include the following information (all files besides your cv should be submitted in one single pdf file):

• a letter of motivation (1 page maximum);
• a curriculum vitae including list of publications and teaching track record;
• a research statement (2 pages maximum);
• a teaching statement (1 page maximum);
• a list of three references from different institutes. References do not need to have a letter ready at the time of application.

A knowledge security check can be part of the selection procedure.
(for details:national knowledge security guidelines)

Only complete applications received within the response period via the link will be considered.

If you have any questions or do you require additional information Please contact:

• dr. ir. Erik J. Bekkers Associate professor