Doctoral Position in Mathematics

20.11.2024, Wissenschaftliches Personal

One doctoral position (E12) is available at TUM with starting date 1.1.2025 at the earliest, in association with Professor G. Friesecke. The theme is to combine techniques from optimal transport and deep learning for effective sampling of high-dimensional distributions, within the DFG Programme 'Theoretical Foundations of Deep Learning'.

Doctoral position: Combining optimal transport and deep learning for sampling high-dimensional distributions, Department for Mathematics, TU Munich

One doctoral position (E12) is available at TUM with starting date 1.1.2025 at the earliest (or thereafter), in association with Professor G. Friesecke, for three years. The theme is to combine techniques from optimal transport and deep learning for effective sampling of high-dimensional distributions. A guiding example is the Boltzmann distribution from molecular dynamics which governs the equilibrium behaviour of large molecular systems. The project is funded by DFG within the Schwerpunkt (Priority Programme) SPP2298 'Theoretical Foundations of Deep Learning'.

Successful applicants will hold or are about to complete a Masters in mathematics or physics, and will have previous expertise in either optimal transport or deep learning.

Please send a CV and transcript of grades as soon as possible but no later than 15.12.2024 electronically (as a single pdf-file) to: Amadeus Dorchholz, Department of Mathematics, TU Munich, dorc@cit.tum.de. Also please arrange that one brief letter of recommendation be sent directly by your chosen referee (typically the supervisor of your Masters thesis) to the same email address.

TUM explicitly encourages applications from women and all others who would bring additional diversity dimensions to the university.

Kontakt: Amadeus.Dorchholz@tum.de