
Are you interested in understanding the dynamics of deep learning using general mathematical principles, such as symmetry and universality? Are you interested in using deep learning to tackle difficult scientific problems? AMLab is hiring a 2-year postdoc to work with Dr. Nabil Iqbal on a project supported by the Simons Foundation grant Symmetries and their breaking in deep learning.

Are you interested in understanding the dynamics of deep learning using general mathematical principles, such as symmetry and universality? Are you interested in using deep learning to tackle difficult scientific problems? AMLab is hiring a 2-year postdoc to work with Dr. Nabil Iqbal on a project supported by the Simons Foundation grant Symmetries and their breaking in deep learning.
Artificial neural networks models are made out of many individually simple constituents which assemble together into emergent entities with remarkable capabilities, including the ability to converse, reason, and accomplish difficult tasks. How can stacks of matrices and non-linearities accomplish such amazing feats? The task of developing a theoretical framework for this emergence – and to then using this understanding to construct more robust, efficient, controllable, and generally better machine learning models – is currently a focus of extremely intense study. Philosophically similar problems involving the emergence of non-trivial collective behavior from simple microscopic ingredients have been studied for decades in the neighbouring field of physics. In this project, you will take inspiration from those ideas – and in particular the general unifying principle of symmetry – to tackle these difficult problems.

Artificial neural networks models are made out of many individually simple constituents which assemble together into emergent entities with remarkable capabilities, including the ability to converse, reason, and accomplish difficult tasks. How can stacks of matrices and non-linearities accomplish such amazing feats? The task of developing a theoretical framework for this emergence – and to then using this understanding to construct more robust, efficient, controllable, and generally better machine learning models – is currently a focus of extremely intense study. Philosophically similar problems involving the emergence of non-trivial collective behavior from simple microscopic ingredients have been studied for decades in the neighbouring field of physics. In this project, you will take inspiration from those ideas – and in particular the general unifying principle of symmetry – to tackle these difficult problems.
You will build a line of research that is oriented towards understanding the dynamics of modern neural architectures in a mathematically principled manner, and further you will use this new understanding to build new deep learning architectures with an eye towards scientific applications.
The precise directions will be chosen to align with your research interest but some potential ideas include:
This list of topics is to give you an idea of the interests of the group and is by no means exclusive.
To accomplish these research goals you will:
You will be part of a new group within AMLab that is being started by Nabil Iqbal to study neural networks from a physical point of view.
If you have questions we encourage you to contact Nabil at [email protected].
The start date is Fall 2027.
We offer a temporary employment contract 38 hours per week for a period of 24 months with a probationary period of two months. The preferred starting date is Fall 2027.
The gross monthly salary, based on 38 hours per week and dependent on relevant experience, ranges between €3,706 to € 5,760 (scale 10). This does not include 8% holiday allowance and 8,3% year-end allowance. The UFO profile Researcher 4 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 more about the benefits.
You will build a line of research that is oriented towards understanding the dynamics of modern neural architectures in a mathematically principled manner, and further you will use this new understanding to build new deep learning architectures with an eye towards scientific applications.
The precise directions will be chosen to align with your research interest but some potential ideas include:
This list of topics is to give you an idea of the interests of the group and is by no means exclusive.
To accomplish these research goals you will:
You will be part of a new group within AMLab that is being started by Nabil Iqbal to study neural networks from a physical point of view.
If you have questions we encourage you to contact Nabil at [email protected].
The start date is Fall 2027.
We offer a temporary employment contract 38 hours per week for a period of 24 months with a probationary period of two months. The preferred starting date is Fall 2027.
The gross monthly salary, based on 38 hours per week and dependent on relevant experience, ranges between €3,706 to € 5,760 (scale 10). This does not include 8% holiday allowance and 8,3% year-end allowance. The UFO profile Researcher 4 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 more about the benefits.
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.
You will be part of the Amsterdam Machine Learning Lab (AMLab). 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
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.
You will be part of the Amsterdam Machine Learning Lab (AMLab). 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
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 red button. We accept applications until and including 8 December 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 red button. We accept applications until and including 8 December 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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