Doctoral Researcher in AI-Driven Conceptual Structural Design for Steel Reuse

Login | Support Help Documentation Suche MyTUM- Online services and login pages Online services and login pages Bulletin board Bulletin board Contacts and support Contacts and support Sitemap > Jobs und Stellenangebote > Academic staff > Back to News Board Browse in News Doctoral Researcher (m/f/d) in AI-Driven Conceptual Structural Design for Steel Reuse 01.09.2026, Academic staff We invite applications for a 75% Doctoral Researcher position in AI-Driven Conceptual Structural Design for Steel Reuse. Funded for three years, the position is expected to commence on 15 January 2027. Are you passionate about computational structural design and eager to shape the future of digital construction? Do you have a strong interest in artificial intelligence, structural optimisation, and the adaptive reuse of structures? Would you like to contribute to an ambitious interdisciplinary research project at the intersection of structural engineering, architecture, and computation? We are establishing a research team to develop an AI-driven conceptual design platform that automatically generates efficient, code-compliant structural schemes from inventories of reclaimed steel elements. By integrating machine learning, structural optimisation, and automated code-compliance verification, the platform will enable designers to create structural designs based on available reclaimed steel inventories rather than sourcing materials for a predefined design, thereby advancing circular steel construction. The research project, promoted by the Institute for Advanced Study at the Technical University of Munich (TUM) with funding from the TÜV Süd Foundation, is led by Prof. Dr. Vittoria Laghi and Prof. Dr. Pierluigi D’Acunto. About us Prof. Dr. Vittoria Laghi is a Hans Fischer Fellow at the TUM Institute for Advanced Study and an Assistant Professor of Structural Design at the University of Bologna, where she is a member of the Additive Manufacturing and Automation in Construction (AMAC) research group. The group investigates advanced technologies for both new and existing construction, with a particular emphasis on large-scale metal 3D printing and computational design tools that support sustainable and circular construction practices. Prof. Dr. Pierluigi D’Acunto is a Rudolf Mößbauer Fellow at the TUM Institute for Advanced Study and Head of the Professorship of Structural Design at the TUM School of Engineering and Design. His research group explores innovative structural design and construction strategies that promote the efficient and sustainable use of material resources. A central focus of its work is the relationship between structural form and force flow, investigated through computational form-finding methods and machine learning-supported structural design approaches for advanced manufacturing technologies. What we offer We offer a 3-year, part-time (75%) doctoral research position in an interdisciplinary, internationally oriented academic environment. Your primary workplace will be the Professorship of Structural Design at the Technical University of Munich (TUM), where you will work as part of a dynamic research team and benefit from excellent working conditions. The position also includes one or more research stays with the Additive Manufacturing and Automation in Construction (AMAC) research group at the University of Bologna, providing opportunities for close collaboration within the project’s international research network. Employment is with appropriate remuneration in accordance with the Collective Agreement for the Public Service of the German Federal States (TV-L E13). The position will start on 15 January 2027. At TUM, we are committed to increasing the number of female employees, and we strongly encourage applications from women. The position is suitable for people with disabilities. Disabled applicants will be given preference in case of generally equivalent suitability, aptitude, and professional performance. What we expect from you Candidates should hold a Master of Science (or equivalent doctoral degree) in Structural Engineering, Architectural Engineering, or a closely related discipline. A demonstrated interest in computational structural design and strong programming skills, particularly in Python, are essential. Experience in steel engineering, geometry processing, additive manufacturing, and machine learning is highly desirable. Excellent written and spoken English skills are required; proficiency in German is advantageous but not mandatory. We are looking for a highly motivated and proactive researcher who thrives in a collaborative, interdisciplinary, and international environment, demonstrates strong problem-solving abilities, and is committed to advancing cutting-edge research. Information about the application process We look forward to receiving your application via e-mail to [email protected] by 20 October 2026. Applications sent to any other email address will not be considered. Your application should include a cover letter (max. 2 pages), CV (max. 5 pages), and a list of relevant publications (max. 5 pages). The maximum file size of your application should not exceed 10 MB, and all documents should be in PDF format. Prof. Dr. Pierluigi D'Acunto Technical University of Munich TUM School of Engineering and Design Professorship of Structural Design Arcisstraße 21, 0507.EG.730 80333 Munich, Germany The position is suitable for disabled persons. Disabled applicants will be given preference in case of generally equivalent suitability, aptitude and professional performance. Data Protection Information: When you apply for a position with the Technical University of Munich (TUM), you are submitting personal information. With regard to personal information, please take note of the Datenschutzhinweise gemäß Art. 13 Datenschutz-Grundverordnung (DSGVO) zur Erhebung und Verarbeitung von personenbezogenen Daten im Rahmen Ihrer Bewerbung. (data protection information on collecting and processing personal data contained in your application in accordance with Art. 13 of the General Data Protection Regulation (GDPR)). By submitting your application, you confirm that you have acknowledged the above data protection information of TUM. 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