PhD student positions in mathematics for artificial intelligence

Information about the project: http://www.chalmers.se/math/. More details about our activities within AI are available at AI@MS.

Provided that sufficient funding is obtained, we will recruit six or more doctoral students in mathematics for artificial intelligence for two projects financed by CHAIR and four projects supported by WASP.

The expansion of Artificial Intelligence (AI), in the broad sense, is one of the most exciting developments of the 21st century. This progress opens up many possibilities but also poses grand challenges. The center "Wallenberg AI, Autonomous Systems and Software Program" (WASP) has launched a program to develop the mathematical side of this area, see http://wasp-sweden.org. The aim is to promote the competence of Sweden as a nation within the area of AI. We are already taking part in this program through four research projects that started last year. We will now open four new projects:

1. Generalization Bounds for Deep Neural Networks: Insight and Design, supervisor: Rebecka Jörnsten and Giuseppe Durisi.

2. Deep Learning for the Optimal Filtering Problem, supervisors: Stig Larsson and Adam Andersson.

3. Mathematics of Shape Learning, supervisor: Klas Modin.

4. Group Equivariant Convolutional Neural Networks, supervisor: Daniel Persson.

We seek four or more PhD students to be nominated for participation in the WASP AI Graduate School.

After our nominations, the final selection of the candidates will be done by WASP. The projects are expected to start on August 15, 2020, but the starting date is flexible.

As a counterpart to WASP, Chalmers has recently launched the Chalmers AI Research Centre (CHAIR) to significantly increase Chalmers’ expertise and excellence in artificial intelligence. We are able to announce two PhD positions for the research projects:

5. Deep Learning and Likelihood-Free Bayesian Inference for Intractable Stochastic Models, supervisor: Umberto Picchini. 

6. Stochastic Continuous-Depth Neural Networks, supervisor: Moritz Schauer. 

Qualifications 
To qualify as a PhD student, you must have a strong background in mathematics, mathematical statistics, or theoretical physics. You must have obtained a master's degree or a 4-year bachelor's degree, or expect to complete such a degree by September 1, 2020.

Depending on the choice of project, experience in some of the following areas (in alphabetical order) may be meritorious: approximation theory, Bayesian inference, complex geometry, computational methods for partial differential equations, differential geometry, gauge theory, group theory, machine learning, Markov chain Monte Carlo methods, neural networks, nonlinear filtering, optimal transport, programming, sequential Monte Carlo, statistical mechanics, stochastic (partial) differential equations. 

You will be considered for all of the projects but it is important that you mention in the application your specific research interests and indicate which of the projects you are most interested in. You should also include all relevant work such as bachelor's or master's thesis and articles (provide an English summary if necessary) that you have authored or co-authored. Evidence of mathematical problem solving skills is significant, besides course grades.

As Chalmers is a highly international environment, proficiency in written and spoken English is necessary. 

READ MORE AND APPLY HERE

Application deadline: 6th March, 2020.  The top ranked candidates will be interviewed in the end of March.