RWTH Aachen - Chair of Mathematics for Uncertainty Quantification
The new DFG International Research Training Group (IRTG) 2379 builds on a unique consortium, at RWTH Aachen University with its Aachen Institute of Advanced Study in Computational Engineering Science, and at the University of Texas at Austin with its Institute for Computational Engineering and Sciences. The projects are embedded in the field of modern inverse problems and introduce a new innovative perspective into the education of future scientists and engineers.
The Mathematics Department of RWTH Aachen University invites applications for a Ph.D. position in "A hierarchical framework for Bayesian Optimal Experimental Design" under the primary supervision of Raul Tempone (Alexander von Humboldt Professor of Mathematics for Uncertainty Quantification at RWTH Aachen University), jointly with Omar Ghattas (Director of the Center for Computational Geosciences in the Institute for Computational Engineering and Sciences (ICES) at The University of Texas, Austin).
Applications for this position must contain a CV, a letter of motivation, copies of degree certificates, and relevant course transcripts along with contact details of at least two academic referees. For full consideration, please apply by May 15, 2020. However, applications will be accepted until the position is filled.
The scope of this Ph.D. project is to develop a hierarchical uncertainty quantification (UQ) framework for complex systems together with the construction of the corresponding hierarchical Bayesian Optimal Experimental Design framework for both static and dynamic inverse problems. As a by-product, a novel stochastic optimization approaches tailored to the Bayesian Optimal Experimental Design setting is to be developed.
This is a full-time 3-year position (salary TVL 13 100%) providing the opportunity to develop personal strengths and engage in a unique graduate school program that provides an international and interdisciplinary working environment. The candidate is expected to have full involvement in the IRTG activities, including joint RWTH-UT colloquia, annual workshops and schools, and short courses. Short research stay at the University of Texas in Austin is also part of the training program.
We are seeking highly motivated candidates with strong mathematical skills. The requirement for this position is a master's degree in engineering, applied mathematics, or physics, or a similar subject with a superior academic record. Practical programming experience is of advantage. Knowledge in uncertainty quantification, inverse problems, Bayesian inference, optimization and/or data analysis is desired. Excellent written and spoken English language skills are required.
What we offer:
The position is for 3 years and is to be filled as soon as possible. This is a full-time position. It is also available as part-time employment per request.
The successful candidate has the opportunity to pursue a doctoral degree.
The salary is based on the German public service salary scale (TV-L).
RWTH Aachen University is certified as a "Family-Friendly University". We welcome applications from all suitably qualified candidates regardless of gender. We particularly welcome and encourage applications from women, disabled persons and ethnic minority groups, recognizing they are underrepresented across RWTH Aachen University. The principles of fair and open competition apply and appointments will be made on merit. RWTH Aachen is an equal opportunities employer. We therefore ask you not to include a photo in your application. For information on the collection of personal data pursuant to Articles 13 and 14 of the General Data Protection Regulation (GDPR), please visit: http://www.rwth-aachen.de/dsgvo-information-bewerbung
Please send your application by May 15, 2020 to
Prof Raul Tempone
RWTH Aachen University
You can also send your application via email to firstname.lastname@example.org. Please note, however, that communication via unencrypted e-mail poses a threat to confidentiality as it is potentially vulnerable to unauthorized access by third parties.