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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.

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 of AI for Science (AI4Science) as an Assistant Professor (UD) within the Informatics 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.

We invite you to help shape the future of AI for Science (AI4Science) as an Assistant Professor (UD) within the Informatics 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;
Your experience and profile:
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 € 4,728 to € 6,433 (scale 11). This does not include 8% holiday allowance and 8,3% 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.
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;
Your experience and profile:
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 € 4,728 to € 6,433 (scale 11). This does not include 8% holiday allowance and 8,3% 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.
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 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 about working at the University of Amsterdam.
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 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 about working 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 30 May 2026.
Applications should include the following information (all files besides your cv should be submitted in one single pdf file):
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 below will be considered.
If you have any questions or do you require additional information? Please contact:
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 30 May 2026.
Applications should include the following information (all files besides your cv should be submitted in one single pdf file):
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 below will be considered.
If you have any questions or do you require additional information? Please contact:
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