Research Data Scientist in Structural Health Monitoring
hace 4 días
Overview: We are seeking a highly motivated and skilled Research Data Scientist to join our team at the Universidad Politecnica de Madrid. The successful candidate will be responsible for developing and integrating synthetic signal generation, machine learning models for damage detection, localization, and quantification, as well as implementing the digital twin for Condition Based Maintenance (CBM) of structures.
Salary and Benefits: The annual gross salary is €35,000, with a possibility of a research stay 6-9 months at BSC-CNS Barcelona. Additionally, the position offers flexible work schedules and Work-Life Balance.
Job Description: As a Research Data Scientist, you will be working on the development and implementation of advanced signal processing, statistical analysis, and machine learning techniques for structural health monitoring. You will collaborate with a multidisciplinary group with several years' experience in the related topics and attend national or international conferences from the second year of the contract.
Required Skills and Qualifications: To be eligible for this position, you must hold a PhD degree in engineering (mechanical, aeronautical, telecommunications, computer science). A C1 level or higher in English, both in written and oral comprehension, is mandatory. Additionally, we require experience in programming languages such as Python, Matlab, and C, as well as demonstrable experience with Linux OS.
Benefits: This role offers a comprehensive approach to structural health monitoring, combining the generation of a large database of damage scenarios with High-Performance Computing (HPC) simulations, and the training of Machine Learning (ML) methods for damage detection, localization, and quantification tasks on complex structures.
Others: We explicitly encourage women, individuals with disabilities, or those with special educational needs to apply, and we ensure non-discrimination in the selection process, retention, and career progression within the institution. The project involves a comprehensive approach, combining the generation of a very large and high-fidelity database of damage scenarios with HPC simulations, and the training of ML methods for damage detection, localization, and quantification tasks on complex structures.
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