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Position: PhD student position (f/m/d) - Nonlinear Optimization and Inverse Problems
Institution: Weierstrass-Institut für Angewandte Analysis und Stochastik
Location: Berlin, Germany
Duties: Project: "Machine Learning for Inverse Problems with continuous normalizing flows and mean field games". The goal of the project is the development and analysis of Neural Networks for invertible measure transport such as normalizing flows. Connections to optimal transport, optimal control, mean field games and stochastic differential equations will be examined. Moreover, low-rank tensor formats will be used in a hybrid method. In collaboration with the PTB, the developed methods will be applied to inverse problems for geometry parameters the quality control of semiconductor manufacturing
Requirements: A completed scientific university degree (master’s degree) in mathematics or a closely related field is required as well as demonstrable programming experience preferably in python and good communication skills in English. An applied mathematical education (in particular numerical analysis, functional analysis or stochastic analysis) is required for a successful application. Moreover, experience in the implementation of machine learning models and numerical algorithms is beneficial
   
Text: Weierstrass Institute for Applied Analysis and Stochastics Leibniz Institute in Forschungsverbund Berlin e. V. The Weierstrass Institute for Applied Analysis and Stochastics (WIAS) is an institute of the Forschungsverbund Berlin e.V. (FVB). The FVB comprises seven non-university research institutes in Berlin which are funded by the federal and state governments. The research institutes are members of the Leibniz Association. WIAS invites applications for a PhD student position (f/m/d) (Ref. 22/17) in the Research Group “Nonlinear Optimization and Inverse Problems” (Head: Prof. Dr. D. Hömberg) starting at the earliest possible date. The position is tied to the project “Machine Learning for Inverse Problems with continuous normalizing flows and mean field games” PI: PD Dr. Martin Eigel). The goal of the project is the development and analysis of Neural Networks for invertible measure transport such as normalizing flows. Connections to optimal transport, optimal control, mean field games and stochastic differential equations will be examined. Moreover, low-rank tensor formats will be used in a hybrid method. In collaboration with the PTB, the developed methods will be applied to inverse problems for geometry parameters the quality control of semiconductor manufacturing. We are looking for candidates with a solid background in applied mathematics, theoretical chemistry, theoretical physics, or electrical engineering. They are expected to be familiar with some of the topics numerical analysis (for differential equations), quantification of uncertainty, statistical learning theory, high-dimensional approximations, stochastic analysis. Applicants are also expected to have experience with at least one of the popular Python frameworks for machine learning. Previous experience in continuum mechanics, thermodynamics, homogenization theory, software engineering, or machine learning are beneficial. A completed scientific university degree (master’s degree) in mathematics or a closely related field is required as well as demonstrable programming experience preferably in python and good communication skills in English. An applied mathematical education (in particular numerical analysis, functional analysis or stochastic analysis) is required for a successful application. Moreover, experience in the implementation of machine learning models and numerical algorithms is beneficial. Queries about the project can be directed to Dr. M. Eigel (Martin.Eigel@wias-berlin.de). The appointment is limited for three years until 31.03.2025. The reduced work schedule is 29,25 hours per week, and the salary is according to the German TVoeD Bund scale. The Institute aims to increase the proportion of women in this field, so applications from women are particularly welcome. Among equally qualified applicants, disabled candidates will be given preference. Please upload your complete application documents (motivation letter, detailed CV, certificates, list of MSc courses and grades, copy of the master‘s thesis or draft, two Weierstrass Institute for Applied Analysis and Stochastics Leibniz Institute in Forschungsverbund Berlin e. V. recommendation contacts) via our applicant portal as soon as possible but not later than May 15, 2022 using the button "Apply online". We are looking forward to your application!
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