Doctoral researcher in Data-Driven Multi-Scale Modelling of Bio-Waste-Derived Metamaterials

https://recruitment.uni.lu/en/details.html?id=QMUFK026203F3VBQB7V7VV4S8&nPostingID=114239&nPostingTargetID=161092&mask=karriereseiten&lg=UK

The computational mechanics group of Prof. Stéphane Bordas is searching for a PhD student to work on the multi-scale computational modelling and design for novel bio-waste-derived meta-structures. The ambitious goal of this project is to develop a predictive model capable of capturing the multi-scale nature of 3D-printed designs, accounting for variations in bio-waste ink composition and fabrication process parameters. The quantities of interest will include the traditional mechanical, thermal and acoustic properties of designs, but interestingly will also aim to quantify the human-centered perspective/perception. The efficiency of the computational framework will be achieved by applying acceleration techniques, including the machine-learning surrogate modelling. The work will be carried out in a wider collaboration with international academic and industrial partners under the auspices of the EIC Pathfinder project.

The doctoral student will be a member of the Doctoral School for Science and Engineering in the doctoral programme in Engineering Sciences aiming to provide interdisciplinary and internationally competitive research training.

Key Responsibilities:

• Conduct research in the field of Multiscale Computational Methods for novel metamaterials as described above
• Develop theories, methods and computational tools
• Participate in project meetings / secondments
• Collaborate with external industrial and academic partners
• Disseminate research findings through publications and conference presentations

For further information about the role, please contact Prof. Stéphane Bordas and Dr. Jakub Lengiewicz.

• A Master’s degree in Mechanical Engineering, Mathematics, Computer Science or related field
• Solid background in mathematical modelling, computational modelling and optimization
• Solid background in finite element analysis
• Strong in programming, e.g., Python, C++, MATLAB, Mathematica,
• Understanding of concepts in machine learning / deep learning

Language Requirements:

Applicants must demonstrate at least B2-level proficiency in English.