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Stuttgart, Germany
Professorship - computing in architecture
at University of Stuttgart

The University of Stuttgart is accepting applications for a tenure track professorship – computing in architecture in Stuttgart, Germany.

Tenure track professorship (W1) – computing in architecture at Institute for Computational Design and Construction (ICD) Cluster of Excellence: Integrative Computational Design and Construction for Architecture (INTCDC) at the earliest convenience.

The University of Stuttgart is a leading technical university in Germany and centrally located in one of Europe’s most important high-tech regions. Strengthening its internationally visible excellence in the area of architecture and intelligent systems is a key strategic goal of the university.

The recently established Cluster of Excellence (INTCDC) bundles the competencies of the University of Stuttgart in architecture, civil engineering, manufacturing and system engineering, computer science and robotics, social sciences and humanities to establish a globally unique centre for advanced research and architectural innovation.

The professorship (W1) with tenure track is dedicated to strengthening the link between architecture and computer science in research and teaching. It is embedded in the various research activities of the Institute for Computational Design and Construction and the Cluster of Excellence (INTCDC) Integrative Computational Design and Construction for Architecture. Connections to the Department of Computer Science and the Cyber Valley Initiative are envisioned.

The professorship’s research centres on the development and integration of computer science methods in computational design and digital construction, with a special focus on the areas of machine learning and artificial intelligence. In teaching, it supports the courses of the Faculty of Architecture and Urban Planning in this subject area. Participation in academic management is expected.

We are looking for an outstanding early career researcher with profound knowledge of computer science methods, experience with machine learning and/or artificial intelligence, and interest in their application in architecture. We expect preliminary work in one of the aforementioned research areas and the ability to collaborate in an interdisciplinary context.
The independent acquisition of research funding and the active participation in the (INTCDC) is also expected, which may include the role of principal investigator in the next funding phase.

The professorship is funded in the context of the joint programme for the promotion of early career researchers by the German federal and state-governments. The position is a tenure track position leading to an appointment as a W3 professor in case of a positive evaluation. The requirements for employment listed in § 51 Baden-Württemberg University Law apply. For more information on the criteria of evaluation and quality management, please visit the website.

Application documents, including the completed information form based on the template provided, please visit here.

Three selected publications, work samples if applicable, a list of successful research grants, a brief description of projects and teaching experiences, should be submitted electronically (max. 10 MB as one PDF) using the ‘apply’ link below.

The application deadline is May 15th, 2019. If you have any further questions, please contact the head of the appointment committee Prof. Achim Menges via email. Please be aware of the risks regarding confidentiality and the integrity of your application contents when sending your application via unencrypted email.

The University of Stuttgart has established a dual career program to offer assistance to partners of those moving to Stuttgart. For more information, please visit the website.

The University of Stuttgart is an equal opportunity employer. Applications from women are strongly encouraged. Severely challenged persons will be given preference in case of equal qualifications.

Information according to article 13 DS-GVO concerning personal data can be found here.

www.uni-stuttgart.de