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Position: Research Assistant Position (f/m/d) for deep learning in partial differential equations for fluid flow simulations
Institution: Weierstrass-Institut für Angewandte Analysis und Stochastik
Location: Berlin, Germany
Duties: Analysis and development of deep learning based solvers (e.g. Physics-Informed Neural Networks) for fluid flow PDEs that govern the manufacturing process of fiber reinforced polymers (e.g. Darcy-Brinkman equations); Analysis and development of combined classic and deep learning PDE solvers to better treat the multiscale nature of these processes; Close collaboration with the project partners in order to transfer the developed methods into industrial
Requirements: A motivated, outstanding researcher with a very good degree and excellent doctorate in mathematics as well as previous experience in the fields mentioned above. Additionally, it is highly desired that the candidate has experience in: analysis and numerical solution of partial differential equations, in particular with regards to computational fluid dynamics; working with dedicated computational fluid dynamics software packages, e.g. openFOAM; optimal control and optimization and their interplay with deep learning
   
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 in the Research Group “Nonsmooth Variational Problems and Operator Equations” (Head: Prof. Dr. M. Hintermüller) applications for a Research Assistant Position (f/m/d) for deep learning in partial differential equations for fluid flow simulations (Ref. 22/14) to be filled at the earliest possible date. The position is associated to the Leibniz Collaborative Excellence project “Machine Learning for Simulation Intelligence in Composite Process Design" a joint interdisciplinary project of the Leibniz-Institut für Verbundwerkstoffe (IVW), the German Research Center for Artificial Intelligence (DFKI), Leibniz-Institut für Polymerforschung Dresden e.V. (IPF), the Fraunhofer Institute for Industrial Mathematics (ITWM) and the Weierstrass Institute for Applied Analysis and Stochastics (WIAS). The work tasks include: • Analysis and development of deep learning based solvers (e.g. Physics-Informed Neural Networks) for fluid flow PDEs that govern the manufacturing process of fiber reinforced polymers (e.g. Darcy-Brinkman equations) • Analysis and development of combined classic and deep learning PDE solvers to better treat the multiscale nature of these processes • Close collaboration with the project partners in order to transfer the developed methods • into industrial We are looking for: A motivated, outstanding researcher with a very good degree and excellent doctorate in mathematics as well as previous experience in the fields mentioned above. Additionally, it is highly desired that the candidate has experience in: • analysis and numerical solution of partial differential equations, in particular with regards to computational fluid dynamics • working with dedicated computational fluid dynamics software packages, e.g. openFOAM • optimal control and optimization and their interplay with deep learning Technical queries should be directed to Prof. Dr. Michael Hintermüller (Michael.Hintermueller@wias-berlin.de). The position is remunerated according to TVöD Bund and is initially limited to two years, while a long-term perspective is envisioned. 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, including cover letter, curriculum vitae and certificates, via our applicant portal as soon as possible but not later than April 30th, 2022 using the button "Apply online". We are looking forward to your application!
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