Umeå University, Mathematics and Mathematical Statistics

Position ID: 2216-PHD1 [#20175]
Position Title: PhD student in mathematics within the WASP AI programme
Position Type: Other
Position Location: Umea, Vasterbottens Lan 901 87, Sweden [map] sort by distance
Subject Areas: Mathematics, focusing on geometric deep learning
Application Deadline: finished (2022/06/23, finished 2023/02/18, listed until 2022/08/15)
Position Description:    

*** this position has been closed. ***

The Department of Mathematics and Mathematical Statistics is opening a PhD position in mathematics, focusing on geometric deep learning. The position involves four years of doctoral studies, including participation in research and postgraduate courses. The last day to apply is August 15, 2022.

Project description and tasks:

Deep learning models, particularly deep convolutional neural networks (CNNs), have enjoyed tremendous success on an impressive number of complex problems. However, a fundamental understanding of the mathematical descriptions of the models and their extensions to non-flat data are still lacking, presenting an exciting research problem spanning several areas such as differential geometry, numerical analysis, and dynamical systems.

A promising approach, referred to as geometric deep learning, is to account for the geometry of the input data by making the networks equivariant with respect to the symmetries of the data. This means that a transformation of the input produces a corresponding transformation of the output. Making such geometric structures manifest amounts to incorporating prior knowledge of the system to facilitate learning.

Another development explores the connection to differential equations and dynamical systems in the limit of infinitely deep networks. Formulated in terms of the dynamics propagating information through the network, the learning problem becomes amenable to powerful numerical techniques for differential equations and a rich theory of dynamical systems.

The project aims to unify the two approaches and develop the mathematical foundations of the emerging field of neural differential equations by exploring the connection to geometric deep learning. The objective is to develop a manifestly geometric formulation of equivariant neural differential equations, unifying symmetries of the data manifold and symmetries of the differential equations, and to investigate the properties and applications of such a formulation.

The project is a part of the AI-Math track within Wallenberg AI, Autonomous Systems and Software Program (WASP). The PhD student will participate in the WASP graduate school, see

Umeå offers excellent working and living conditions. The city is young and located right next to a large river. It is surrounded by forests and lakes and is close by the sea. In the vicinity there are plenty of opportunities for both indoor and outdoor activities.

Further information:

Further information can be provided by Associate Professor Fredrik Ohlsson ( or Professor Jun Yu ( You can also contact the Head of Department Professor Åke Brännström (, for additional information.

For instructions on how to apply, see:

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Contact: Fredrik Ohlsson
Email: email address
Postal Mail:
Department of Mathematics and Mathematical Statistics
Umeå University
901 87 Umeå
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