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:

1. 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?
2. 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.

• 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 [email protected].
The start date is flexible (but no later than Fall 2027) and there are multiple positions available. 

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.