The University of Luxembourg is an international research university with a distinctly multilingual and interdisciplinary character.
The Department of Engineering (DoE) is a dynamic, interdisciplinary group engaged in civil, electrical and mechanical engineering, driving forward innovative research and solutions. It also has an internationally leading profile in computational science and engineering. We develop cutting-edge technologies, promoting the sustainable and economical use of resources, and meeting the technological demands of Luxembourg, the Greater Region, and beyond. Nearly all research projects are supported by a network of national and international partnerships, several with public or industrial stakeholders. At the University, the Department cooperates across faculties and with the interdisciplinary centres. In particular, the new interdisciplinary centre focused on complex environmental systems opens up significant opportunities for future collaborations.

The Faculty of Science, Technology and Medicine of the University of Luxembourg is opening for its Department of Engineering (DoE) a new Associate Professor or Assistant Professor (tenure track) position in Computational Fluid Dynamics.
Advances in computational sciences, machine learning, and artificial intelligence are transforming the study of complex flows. Such systems are prevalent in both industrial physic-chemical processes and natural environments, making this research both ubiquitous and interdisciplinary. The increasing availability of experimental and production data, requires new computational methods, that leverage high-performance computing power, to develop advanced tools.

The successful candidate will be expected to develop machine learning methods that integrate physical understanding, to address challenges in dynamical systems and engineering flows and enable first-principles modeling. This involves:

• Developing machine learning methods that fuse nonlinear dimensionality reduction with dynamical systems learning, to accurately capture complex fluid behaviours
• Designing surrogate models and identifying physically important parameters from information-rich datasets, to enable innovative process design/optimization and uncertainty quantification in CFD
• Extending the capabilities of legacy CFD software, to perform ML-aided stability and sensitivity analysis in real-world industrial scenarios
• Combining CFD and experimental data to develop nonlinear observers, necessary to monitor and control engineering flows
• Envisioning and implementing the current advances in foundational model development (such as LLMs) in engineering systems is also expected, supported by the interactions with the Department of Computer Science in the Faculty of Science Technology and Medicine

Teaching is a vital component of this position. The candidate will be responsible for teaching and developing undergraduate and graduate courses within the Department of Engineering, including courses that bridge engineering and data science. Supervision and mentorship of graduate students and postdoctoral researchers are also key responsibilities, fostering the next generation of engineers and researchers. Additionally, contributing to the administration and development of the department will be expected, participating in committees and other departmental activities.
Contact: Prof. Dr. Francesco Viti: francesco.viti@uni.lu