Research Assistant (f/m/d) - PhD position (DC4) for the development of integral approaches for coupling digital building models and monitoring systems with intelligent sensor systems

The Chair of Structural Analysis and Structural Dynamics at RWTH Aachen University specialises in the research areas of numerical simulation of buildings and complex structures, structural dynamics, earthquake engineering, structural monitoring, condition assessment and analysis of structures under extreme loads. The chair has outstanding expertise in these research areas and operates specialised testing facilities.

The Chair of Structural Analysis and Dynamics is looking for a Doctoral Candidate (DC4) for the Horizon Europe Marie Skłodowska-Curie PhD network "NATECH Risk management and Resilience of High-TECH industries and Critical infrastructures across EUrope" (REUNATECH). The REUNATECH network will train a highly qualified generation of doctoral candidates (DCs) who will be able to tackle future challenges in the advertised field through interdisciplinary cutting-edge research and innovation. REUNATECH brings together leading universities, research institutes and industrial partners from all over Europe and offers an interdisciplinary doctoral programme for the study of the interactions between natural hazards and technological accidents industry (NATECH). Research focuses on the risk assessment and resilience of high-tech industries and critical infrastructures and the development of new smart technologies.

• Completed university degree (Master's degree or equivalent) in civil engineering or related fields.
• You must not hold a PhD at the time of recruitment.
• At the time of recruitment, the researcher must not have resided or carried out their main activity (work, studies, etc.) in Germany for more than 12 months in the last three years immediately prior to the recruitment date. Compulsory national service and/or short stays such as holidays are not considered.
• Proven experience or great interest in the topics of Structural Health Monitoring (SHM), Building Information Modelling (BIM), numerical simulations and real-time monitoring systems with the use of sensor technology.
• Proficiency and/or interest in programming languages (e.g. MATLAB, Python, R) and software platforms such as Autodesk, ALLPLAN, ANSYS, REVIT, Tekla, Rhino, Grasshopper, etc.
• Basic knowledge in the areas of signal processing, time series analysis or machine learning for the interpretation of structural data is desirable.
• Basic knowledge of numerical analysis and design of structures for special load cases (earthquakes, explosions) is desirable.
• You are fluent in written and spoken English. 

Each application must include the following material:

• Curriculum vitae
• Evidence of English proficiency.
• Copy of Bachelor’s and Master’s certificates and transcripts.
• Any additional material useful for the assessment of the candidate (e.g., recommendation letters, research project/statement in agreement with the requirements specified in previous text).

All material must be included in one compiled pdf file. The file should be named indicating the surname of the applicant and the position i.e. Surname_DC4.pdf.

• Carry out independent research work under the supervision of the academic supervisors in cooperation with other PhD students of the REUNATECH network.
• Development and implementation of signal processing algorithms for the continuous condition assessment of load-bearing structures under dynamic loads (e.g. earthquakes).
• Selection and integration of intelligent sensors in digital building models for real-time monitoring.
• Carry out measurements on prototypes and installing the developed systems in a real structure.
• Visualisation of sensor and simulation data in digital building models.
• Writing scientific publications and presenting the results in workshops and at conferences.
• Active participation in REUNATECH training activities and active contribution to co-operation within the network. 

The tasks listed above are not exhaustive, and additional activities and tasks may arise during the course of the doctoral work in order to achieve the research objectives of the advertised position. These are then to be carried out in consultation with the advisors. 

The successful candidate will be employed under a regular employment contract.
The position is to be filled by 1/1/2027 and offered for a fixed term for three years.
The fixed-term employment is possible as it constitutes one of the fixed-term options of the Wissenschaftszeitvertragsgesetz (German Act on Fixed-term Scientific Contracts).
This is a full-time position.
The successful candidate has the opportunity to pursue a doctoral degree in this position.
The salary is based on the German public service salary scale (TV-L).
The position corresponds to a pay grade of EG 13 TV-L.

RWTH is proud to be certified as a family-friendly university. As part of our Campus Health initiative, RWTH offers a wide range of health, counseling, and prevention services, including university sports. Employees under collective bargaining agreements and civil servants also benefit from a comprehensive continuing education program and the option to purchase a job ticket for convenient commuting.
RWTH is an equal opportunities employer. We welcome and encourage applications from all qualified candidates and make employment decisions without regard to national or ethnic origin, sex, sexual orientation, gender identity, religion, disability or age. RWTH is especially committed to supporting the advancement of women in academia. When women are underrepresented in a university unit, preferential consideration will be given to female applicants among candidates of equal merit and professional standing, unless compelling reasons favor another individual.
In keeping with RWTH’s commitment  to equality of opportunity, we ask that you do not include a photo with your application.
Please refer to https://www.rwth-aachen.de/applicant-privacy-policy for information about how we collect and process applicant data in accordance with Articles 13 and 14 of the European Union’s General Data Protection Regulation (GDPR).

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