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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Job offer - **Company:** TU Delft - **Location:** Netherlands - **Contract:** Temporary contract - **Skills:** Computer Simulation, Computer Programming, Python (Programming Language), MATLAB, Machine Learning, Digital Twin, Data Processing, Model Validation, Information Technology, Tools for Reporting - **Published:** June 5, 2026 - **Apply:** https://euraxess.ec.europa.eu/jobs/442543 ## About the Role * A Ph.D. in Mechanical Engineering, Applied Mathematics, Computer Science, or a related field. * Strong background in surrogate modlling and digital twin development and numerical simulation. * Experience with FEM and/or Multi-body software for dynamic simulations is preferrable. * Proficiency in programming languages such as Python, MATLAB, or similar. * Demonstrated ability to conduct independent research and publish findings in peer-reviewed journals. * Excellent problem-solving skills and attention to detail. * Strong communication and teamwork abilities. * Experience in project management is an advantage., * MSc + PhD degree. * 300 words abstract of your MSc and PhD thesis. * publication list. * two selected publications. * proof of English language proficiency (TOEFL, IELTS or Cambridge). If your MSc and PhD diploma and transcript are not in Dutch, English, French or German and you will be the selected candidate, the TU Delft will ask you to deliver a certified translation in case you will be appointed. ## Description The successful candidate will contribute to the XCROSS project, part of an EU-funded international innovation programme involving collaboration with partners across Europe. The position focuses on the work package "Development of Advanced Digital Twin," with emphasis on developing surrogate models, machine-learning-based prediction tools, and optimization routines for railway crossing asset management applications., * Surrogate Model Development: Develop fast surrogate models and machine-learning-based tools for wheel-rail interaction and crossing degradation prediction. * Optimization and Data Processing: Create optimization routines for crossing geometry profiles using 3D scan inspection data to improve maintenance and asset management strategies. * Programming and Tool Development: Develop and implement numerical and data-driven tools in Python for digital twin applications and engineering workflows. * Model Validation and Integration: Validate developed models and integrate them into the XCROSS digital twin framework in collaboration with project partners. Candidates should have demonstrated experience in machine learning, surrogate modelling, optimization, and Python programming, preferably with existing developed tools or relevant project experience. The Department of Engineering Structures focuses on the development of resilient, smart and sustainable structures and infrastructures. Our aim is to meet societal demands in transportation, the energy transition and sustainable reuse. Research themes include dynamics of structures, mechanics of materials related to e.g. climate change, modelling and design of railway systems, multi-scale modelling of pavement materials and structures, reuse of materials, structures and parts of structures, assessment methods for structures, smart monitoring techniques, design methods, replacement and renovation of civil infrastructure and development of new materials and maintenance techniques. Our unique Macro Mechanics Laboratory facilities support full-scale testing, monitoring and modelling of structures to facilitate implementation of innovations. The department delivers both groundbreaking research and world-class education for undergraduate and graduate students. As a team, we represent different backgrounds, skills and views. We foster an inclusive culture, as our combined identities, attitudes and ambitions widen our perspective and make up our strengths. Railway is the backbone of sustainable mobility. The section of Railway Engineering deals with the physical assets of the railway system, including track, embankment, rolling stock and catenary, as well as the interfaces and dynamic interactions between them. The research, innovation, development and education of the section concern the whole life cycle of the assets, from design, construction, degradation, monitoring, maintenance to retrofit, as well as the data-driven intelligent management of the assets, taking into consideration the performance of the whole railway system. The section plays a leading role in the TU Delft Rail Institute (DelftRail)., * As part of knowledge security, TU Delft conducts a risk assessment during the recruitment of personnel. We do this, among other things, to prevent the unwanted transfer of sensitive knowledge and technology. The assessment is based on information provided by the candidates themselves, such as their motivation letter and CV, and takes place at the final stages of the selection process. When the outcome of the assessment is negative, the candidate will be informed. 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