学界招聘|斯图加特大学招募博士后

The University of Stuttgart represents outstanding, world-renowned research and first-class teaching in one of Europe's most dynamic industrial regions. As a reliable employer, the university supports and promotes the academic careers of its researchers. It is proud of its employees, who currently come from over 100 different countries. The university is a partner for knowledge and technology transfer and focuses on multidisciplinarity.

Our research and teaching address a wide range of directions in modern applied mathematics: Mathematical modelling, applied analysis, stochastic techniques and scientific computing provide the theoretical and practical background to understand, predict and optimise highly complex processes in engineering, life, social and natural sciences. Numerical simulation has become the key technology to address these core questions of the University’s scientific and strategic orientation.

Our research involves developing, analyzing and implementing new numerical techniques in both problem-specific and more fundamental settings, and we are equally interested in multidisciplinary applications and theoretical foundations, connecting engineering, natural and computer sciences. We are involved in various national, international and industrial collaborations.

This project deals with certified a posteriori error estimations of nonlinear eigenvalue problems arising in Density-Functional Theory (DFT) of electronic structure calculation. The project builds upon preliminary work established for the simpler Gross-Piteavskii eigenvalue problem. The goal is to provide guaranteed bounds of the error between the approximate energy and the exact energy.

DFT models are dominantly solved using the so-called Self-Consistent Field (SCF) iterations where a linear eigenvalue problem is solved at each step within the SCF-iterative procedure. The estimator will be designed such that each error component of the total error can be quantified, namely the discretization error due to the planewave discretization, the iteration error due to the non-converged SCF-iterations, and the error due to the iterative solver of the linear eigenvalue problems. This splitting allows the design of an adaptive algorithm with error balancing between the different error sources. The estimator will be a guaranteed upper bound of the error for convex DFT-models and the final goal will be the extension to non-convex models. The developed methods and estimators will be implemented and tested in the open-source DFTK software package, written in Julia.

Starting date is 01.06.2023 or later. If you're interested, please submit a CV, motivation letter, transcript, and up to 3 email contacts for recommendation through the official submission process ("Apply now" button below).

• Excellent MSc degree in applied mathematics (PhD in applied mathematics for post-doc applications).

• Strong interests in numerical analysis, computational mathematics and their application to computational chemistry.