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Project background…
Flow and transport computations for explicit microstructures of stochastic porous media (here, the Knee meniscus) are prohibitively time consuming. First, as the porous microstructure is geometrically complex, each simulation is considerably time consuming. Second, the porous microstructure is intrinsically stochastic as it varies from location to location and from specimen to specimen, requiring numerous time-consuming experiments and flow computations to be performed. PorSol proposes a data driven framework in which the porous microstructures are homogenised to enable simulations in which the explicit microstructural representation is omitted, but the stochastic transport characteristics are preserved.
Machine learning methods have recently been successful in predicting fluid flow and fluid solid structure interaction for different application, however recent development misses such stochastic porous media problem. We propose to incorporate machine learning and data driven building a model which can predict the fluid flow and fluid solid interaction of porous media.
Your Role...
The successful candidate will join the team led by Prof. Andreas Zilian by contributing to the “Numerical homogenisation framework for characterising transport properties in stochastic porous media. - PorSOL” project granted by Luxembourg National Research Fund (FNR). It is expected for the candidate to perform following tasks:
- Contribution to research of the area of data science and artificial neural network
- Contribution to building the data driven model which can predict the fluid flow in porous media
- Publication of articles in scientific papers
- Supporting in the activities taking part in the research group
What we expect from you…
- A PhD degree in Data Science and Machine Learning, Computer Science, Engineering or a related field
- Competitive research record in one of the following areas: Reduced Order Modelling (including meta-models and artificial neural networks) and/or Computational Fluid Dynamics
- Relevant knowledge of Wind Engineering will be treated as a benefit
- Strong development skills in Matlab, Python and/or C++
- Documented research experience in a number of the aforementioned topics Commitment, team working and a critical mind
- Fluent written and verbal communication skills in English are mandatory
- Willingness to cooperate actively in a team of mixed experience
- Ability to meet deadlines and demonstrate methodological, organised, pragmatic and effective approaches to the work
In Short...
- Contract Type: Fixed Term Contract 12 Month
- Work Hours: Full Time 40.0 Hours per Week
- Foreseen starting date: September 2022
- Location: Belval
- Job Reference: UOL04813
The yearly gross salary for every Postdoctoral Researcher at the UL is EUR 75.285 (full time)
How to apply...
Applications should include:
- A full curriculum vitae
- A cover letter indicating your motivation for this project, relevant experience and future interests
- A portfolio of prior work with descriptions of your contributions
- Contact information for 1-3 professional references
Early application is highly encouraged, as the applications will be processed upon reception. Please apply ONLINE formally through the HR system. Applications by email will not be considered.
The University of Luxembourg embraces inclusion and diversity as key values. We are fully committed to removing any discriminatory barrier related to gender, and not only, in recruitment and career progression of our staff.
In return you will get…
- Multilingual and international character. Modern institution with a personal atmosphere. Staff coming from 90 countries. Member of the “University of the Greater Region” (UniGR).
- A modern and dynamic university. High-quality equipment. Close ties to the business world and to the Luxembourg labour market. A unique urban site with excellent infrastructure.
- A partner for society and industry. Cooperation with European institutions, innovative companies, the Financial Centre and with numerous non-academic partners such as ministries, local governments, associations, NGOs …
Further information...
For further information, please contact anas.obeidat@uni.lu
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