PhD position on Theoretical physics and Algorithms of deep learning

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We are looking for an enthusiastic candidate for 1 PhD position at the intersection of Theoretical physics, Astroparticle physics and Deep learning, at the University of Amsterdam. You will be embedded in the Institute for Theoretical Physics (ITFA) and the GRAPPA Center of excellence in Gravitation and Astroparticle Physics. Researchers at ITFA/GRAPPA cover a variety of research topics including Astroparticle physics, Cosmology, Mathematical physics and Theoretical particle physics.

The position is supposed to start by fall 2021.

What are you going to do?

The position will be part of the Institute of Physics PhD project 'Probabilities, Neural Nets and Equivariance', under joint supervision of Dr Miranda Cheng and Dr Christoph Weniger.

Theoretical physics concepts have in many ways inspired modern machine learning techniques, ranging from Hamiltonian Monte Carlos to equivariant neural networks. At the same time, machine learning techniques like deep neural networks and differentiable programming play an increasingly important role in enabling breakthrough discoveries in fundamental (astro-)physics. This cross-fertilization of physics and machine learning is a very active area of research. Goal of this PhD project is to become fluent in both worlds, and to explore new connection points at their intersection, with applications to lattice field theories and dark matter searches. Candidates interested in theoretical physics, deep learning algorithms and dark matter searches.

Candidates interested in Theoretical physics, Deep learning algorithms and Dark matter searches are invited to apply.

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