We are excited to offer a PhD position within the EU-funded SmartEM project. SmartEM aim is to use AI-assisted methods to create surrogate models from heterogeneous data sources. The PhD position is focused on combining generative and symbolic AI methods for cost-effective, explainable and reliable modeling for complex industrial systems.

Your role will involve creating efficient, reusable and accurate surrogate models from diverse data sources. The aim is to improve the understanding and optimization of complex industrial systems, where the current common practice is to employ very complex simulations in the design process for tuning and evaluating different design parameters. Surrogate models are simple and efficient models that could in principle replace complex and heavy simulation models. Our goal is to develop and evaluate surrogate models in an industrial setting. 

You will work towards the future of industrial process simulation by blending generative and symbolic AI techniques for developing and evaluating surrogate models. Possible research areas include:

• Developing generative methods to contextualize surrogate models.
• Merging machine learning with symbolic reasoning for reliable and explainable training of accurate surrogate models for individual components, and monitoring of complex systems composed of direct and surrogate models.
• Advancing adaptive surrogate modeling to maintain alignment with actual system changes.

You PhD work will take place within the Interconnected Resource-aware Intelligent Systems (IRIS) cluster of the TU/e Department of Mathematics and Computer Science. The topic of the PhD position will be tailored to your research interests. The project offers collaboration with industrial partners, providing a chance to test your models in real-world scenarios. You will also collaborate closely with another SmartEM PhD candidate working on integrating machine learning with symbolic AI methods for reliable surrogate modeling.

As a successful candidate, you will:

• Conduct high-impact scientific research on AI-Assisted Surrogate Modeling, ensuring practical validation of your findings in the SmartEM project.
• Contribute to publishing results at (international) conferences and journals.
• Collaborate with the other PhDs and researchers in this project.
• Collaborate with other groups and faculty members.
• Collaborate with selected SmartEM project partners, attend project meetings and contribute to deliverables and project outcome.
• Assist with educational tasks (e.g. support lab/course assignments and/or supervise (under)graduate/internships students).

• A master's degree in Computer Science, Data Science, or a related field.
• A strong background in machine learning (specifically deep learning) and/or symbolic reasoning.
• Good programming skills and experience (Python, C, etcetera).
• Strong analytical and implementation skills.
• Good communicative skills and eagerness to work as part of a research team.
• Motivated to develop your teaching skills and coach students.
• Fluent in spoken and written English.

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:

• Full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months. You will spend 10% of your employment on teaching tasks.
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. €2,872 max. €3,670).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
• An excellent technical infrastructure, on-campus children's day care and sports facilities.
• An allowance for commuting, working from home and internet costs.
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.