Independent Junior Research Group Leader (m/f/d, E14 TV-L, 100%) on Machine Learning Methods for Design of Building Structures

Description

Offer Description: Independent Junior Research Group Leader (m/f/d, E14 TV-L, 100%) on Machine Learning Methods for Design of Building Structures The University of Stuttgart stands for excellent, globally recognized research and first-class teaching in one of Europe's most dynamic industrial regions. With over 22,000 students and more than 5,800 employees, we are a reliable partner for knowledge and technology transfer and one of the largest employers in the state capital. The Cluster of Excellence IntCDC - Integrative Computational Design and Construction for Transformative Architecture , which is funded by the German Research Foundation (DFG) from 2019 to 2032 as part of the federal and state governments´ Strategy for Excellence, bundles the internationally recognised competencies of the University of Stuttgart, the Max Planck Institute for Intelligent Systems and Bauhaus Earth. It aims to lay the methodological foundations for the urgently needed, future-proof transformation of the way buildings are designed and constructed. Based on highly interdisciplinary and integrative research, IntCDC seeks to harness the full potential of digital technologies to concurrently advance computational design and engineering methods, cyber-physical fabrication, construction and renovation processes, and next generation material and building systems ( www.intcdc.uni-stuttgart.de ). To complement our research activities, we are seeking a highly motivated scientist with an excellent PhD degree, an outstanding early-career track record and substantial international research experience as Independent Junior Research Group Leader in the field of Machine Learning Methods for Design of Building Structures. We are looking for a researcher with a background in C ivil Engineering or Architecture who can contribute to the application of AI and ML in the design and analysis of building structures. The successful candidate will bridge structural design and data-driven AI and demonstrate excellent research expertise in structural engineering together with one or more of the following fields: Computational design, analysis and optimization methods for building structures Data-driven AI methods, for example physics-informed neural networks Computer vision and geometry processing Safety and reliability concepts for building structures and data-driven decision-making Integration of the aforementioned methods for generative design and assessment of building structures The successful candidate will lead a new Independent Research Group within the Cluster of Excellence IntCDC and its related structures. The establishment of a competitive research program and the proactive collaboration with IntCDC partners at the University of Stuttgart, the Max Planck Institute for Intelligent Systems and Bauhaus Earth is expected. The research group will benefit from IntCDC’s supporting structures, instrumentation and labs. The scientific language of IntCDC is English. The full-time position is on a fixed-term contract until 31 December 2032 . The start date is as soon as possible, but can be adjusted to suit the availability of the successful candidate. To apply, submit your application to [email protected] as one single PDF file (max. 10 MB) comprising (1) a cover letter, (2) an academic CV, (3) a full list of publications, (4) a description of past and future research activities, (5) degree certificates, and transcripts of records from Bachelor/Master/PhD degrees, (6) reprints of up to 3 key publications, (7) other work samples if applicable, (8) three reference letters. Please be aware of the risks regarding confidentiality and the integrity of your application contents when sending your application via email. Please use “IntCDC JRGL Machine Learning Methods for Design of Building Structures” as the subject of your application email. Applications will be reviewed on a rolling basis. The initial application deadline is 15 November 2026 . Information on how we ha

Sectors

Source

Source: euraxess.ec.europa.eu