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Technical University of Munich

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Doctoral Researcher in AI-Driven Conceptual Structural Design for Steel Reuse Technical University of Munich in Germany

Degree Level

PhD

Field of study

Computer Science

Funding

The position is funded for three years at 75% part-time. Employment is according to TV-L E13 remuneration. The project is funded by the TÜV Süd Foundation and includes one or more research stays at the University of Bologna.

Deadline

Oct 20, 2026

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Country

Germany

University

Technical University of Munich

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Keywords

Computer Science
Environmental Science
Mechanical Engineering
Materials Science
Artificial Intelligence
Civil Engineering
Structural Engineering
Architecture
Additive Manufacturing
Architectural Engineering
ML

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About this position

Technical University of Munich (TUM) is inviting applications for a 75% Doctoral Researcher position in AI-Driven Conceptual Structural Design for Steel Reuse. The project sits at the intersection of civil engineering, architecture, computer science, materials science, and environmental science, with a strong focus on computational structural design, structural optimization, machine learning, and the adaptive reuse of reclaimed steel elements.

The research aims to develop an AI-driven conceptual design platform that can automatically generate efficient, code-compliant structural schemes from inventories of reclaimed steel. This supports circular construction by shifting design from material sourcing for a predefined structure to design based on available reused elements. The project is promoted by the Institute for Advanced Study at TUM and funded by the TÜV Süd Foundation.

The doctoral position is led by Prof. Dr. Vittoria Laghi and Prof. Dr. Pierluigi D'Acunto. The work will be based primarily at the Professorship of Structural Design at TUM, with one or more research stays at the Additive Manufacturing and Automation in Construction (AMAC) research group at the University of Bologna.

Funding details: the position is funded for three years, part-time at 75%, with remuneration according to TV-L E13. The start date is expected to be January 15, 2027.

Eligibility highlights include a Master of Science (or equivalent doctoral degree) in Structural Engineering, Architectural Engineering, or a closely related field; strong Python programming skills; and a demonstrated interest in computational structural design. Experience in steel engineering, geometry processing, additive manufacturing, and machine learning is highly desirable. Excellent English is required; German is helpful but not mandatory.

To apply, send your application by email to [email protected] by 20 October 2026. Include a cover letter (max. 2 pages), CV (max. 5 pages), and a list of relevant publications (max. 5 pages) as PDF files, with total size under 10 MB. Applications sent to any other address will not be considered.

Funding details

The position is funded for three years at 75% part-time. Employment is according to TV-L E13 remuneration. The project is funded by the TÜV Süd Foundation and includes one or more research stays at the University of Bologna.

What's required

Applicants should hold a Master of Science or equivalent doctoral degree in Structural Engineering, Architectural Engineering, or a closely related discipline. Strong programming skills, particularly in Python, are essential, along with a demonstrated interest in computational structural design. Experience in steel engineering, geometry processing, additive manufacturing, and machine learning is highly desirable. Excellent written and spoken English are required; German is advantageous but not mandatory. The role seeks a highly motivated, proactive researcher who works well in collaborative, interdisciplinary, international settings and has strong problem-solving abilities.

How to apply

Apply by email to [email protected]. Send a cover letter (max. 2 pages), CV (max. 5 pages), and a list of relevant publications (max. 5 pages) as PDF files, with total application size not exceeding 10 MB. Applications sent to any other email address will not be considered.

More information can be found here

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