Join Us!
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 use 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.
This is what you will do
You will work to understand the dynamics of modern neural architectures in a mathematically principled manner, and further you will use this understanding to build new deep learning architectures. Your project will involve a tight feedback loop between theory and experiment.
Some concrete questions include:
- Symmetry breaking in deep learning: a central problem in deep learning is the coherent propagation of information through many layers, and the integration of information across space and time to perform a computation. Nature solves this problem in various ways, which often include the spontaneous breaking of symmetries, a topic which lies at the heart of our understanding of the phases of matter in the natural world. Recent work has shown that some of the same principles can be applied to deep learning. Can we exploit these ideas to construct new physically-inspired neural network architectures for efficient computation?
- Generative models and non-reciprocal thermodynamics:despite their amazing empirical successes, the fundamental principles underlying the operation of generative models remains somewhat unclear. Is there a theory for how generative models such as diffusion models and autoregressive text generation generalize from their training set? Can simple dynamic principles constrain the creation of structure in such a generative model? Can we obtain insights into the process of generalization from fundamental principles in statistical physics and use these insights to build new kinds of generative models?
Your precise projects will be chosen in cooperation with the PI (Nabil Iqbal) to reflect mutual interests, and you will work together with other students and postdocs in a new group within AMLab that will study deep learning from a physical point of view.
Tasks and responsibilities:
- Conducting independent theoretical research, including development of novel algorithms and mathematical study of the dynamics of deep learning
- Conducting independent empirical research, including coding, training and evaluating deep learning models on a on a high-performance computing cluster.
- Presenting research results at international conferences, workshops, and journals.
- Completing a PhD thesis within the appointed duration of four years
- Assisting in teaching activities, such as teaching labs and tutorials or supervising bachelors and master students.
What we ask of you
- You have an MSc in artificial intelligence, computer science, physics, mathematics, or a related field.
- You have strong mathematical skills and are comfortable with building mathematical models of complex phenomena.
- You have a working knowledge of machine learning, including modern deep learning architectures such as transformers.
- You have sufficient programming skills in Python or related languages. Familiarity with scientific libraries (e.g. NumPy/SciPy, PyTorch or JAX) is preferred.
- You are interested in interdisciplinary research, crossing AI/Machine Learning, computer science, mathematics, and physics
- You are fluent in the English language, both in speaking and writing.
- You are willing to take a light education task, as teaching assistant and in supervising Bachelor’s and Master’s students in their thesis research work.
Familiarity with physics ideas such as spontaneous symmetry breaking is preferred but not required. We are looking for candidates who are excited by the idea of crossing disciplinary boundaries and welcome nontraditional backgrounds or preparation.
If you are unsure whether you qualify we encourage you to contact Nabil at n.iqbal@uva.nl.
The start date is flexible (but no later than Fall 2027) and there are multiple positions available.
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 flexible but could be 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 more about 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.
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, causal inference, reinforcement learning, graph neural networks, and geometric deep learning. You will be part of a new group founded by Nabil Iqbal working at the intersection of deep learning and physics.
Want to know more about our organisation? Read more about working at the University of Amsterdam.
Are you interested in understanding the dynamics of deep learning using general mathematical principles, such as symmetry and universality? AMLab is hiring multiple PhD students to work with Dr. Nabil Iqbal on a project supported by the Simons Foundation grant Symmetries and their breaking in deep learning.
Join Us!
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 use 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.
This is what you will do
You will work to understand the dynamics of modern neural architectures in a mathematically principled manner, and further you will use this understanding to build new deep learning architectures. Your project will involve a tight feedback loop between theory and experiment.
Some concrete questions include:
- Symmetry breaking in deep learning: a central problem in deep learning is the coherent propagation of information through many layers, and the integration of information across space and time to perform a computation. Nature solves this problem in various ways, which often include the spontaneous breaking of symmetries, a topic which lies at the heart of our understanding of the phases of matter in the natural world. Recent work has shown that some of the same principles can be applied to deep learning. Can we exploit these ideas to construct new physically-inspired neural network architectures for efficient computation?
- Generative models and non-reciprocal thermodynamics:despite their amazing empirical successes, the fundamental principles underlying the operation of generative models remains somewhat unclear. Is there a theory for how generative models such as diffusion models and autoregressive text generation generalize from their training set? Can simple dynamic principles constrain the creation of structure in such a generative model? Can we obtain insights into the process of generalization from fundamental principles in statistical physics and use these insights to build new kinds of generative models?
Your precise projects will be chosen in cooperation with the PI (Nabil Iqbal) to reflect mutual interests, and you will work together with other students and postdocs in a new group within AMLab that will study deep learning from a physical point of view.
Tasks and responsibilities:
- Conducting independent theoretical research, including development of novel algorithms and mathematical study of the dynamics of deep learning
- Conducting independent empirical research, including coding, training and evaluating deep learning models on a on a high-performance computing cluster.
- Presenting research results at international conferences, workshops, and journals.
- Completing a PhD thesis within the appointed duration of four years
- Assisting in teaching activities, such as teaching labs and tutorials or supervising bachelors and master students.
What we ask of you
- You have an MSc in artificial intelligence, computer science, physics, mathematics, or a related field.
- You have strong mathematical skills and are comfortable with building mathematical models of complex phenomena.
- You have a working knowledge of machine learning, including modern deep learning architectures such as transformers.
- You have sufficient programming skills in Python or related languages. Familiarity with scientific libraries (e.g. NumPy/SciPy, PyTorch or JAX) is preferred.
- You are interested in interdisciplinary research, crossing AI/Machine Learning, computer science, mathematics, and physics
- You are fluent in the English language, both in speaking and writing.
- You are willing to take a light education task, as teaching assistant and in supervising Bachelor’s and Master’s students in their thesis research work.
Familiarity with physics ideas such as spontaneous symmetry breaking is preferred but not required. We are looking for candidates who are excited by the idea of crossing disciplinary boundaries and welcome nontraditional backgrounds or preparation.
If you are unsure whether you qualify we encourage you to contact Nabil at n.iqbal@uva.nl.
The start date is flexible (but no later than Fall 2027) and there are multiple positions available.
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 flexible but could be 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 more about 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.
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, causal inference, reinforcement learning, graph neural networks, and geometric deep learning. You will be part of a new group founded by Nabil Iqbal working at the intersection of deep learning and physics.
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.
Applications should include the following information (all files besides your cv should be submitted in one single pdf file):
- a detailed CV including the months (not just years) when referring to your education and work experience;
- a letter of motivation, including a description of what makes you a fit for the position
- a list of publications;
- a complete record of your Bachelor and Master courses, included grades, and explanation of the grading system;
- the names and email addresses of two references who can provide letters of recommendation.
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:
