SD-26067 – PHD STUDENT – MULTI-SCALE MODELLING OF MECHANICAL CLOAKING META-STRUCTURES

The Luxembourg Institute of Science and Technology (LIST) is a Research and Technology Organization (RTO) active in the fields of materials, environment and IT. By transforming scientific knowledge into technologies, smart data and tools, LIST empowers citizens in their choices, public authorities in their decisions and businesses in their strategies.

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Your LIST benefits

• An organization with a passion for impact and strong RDI partnerships in Luxembourg and Europe that works on responsible and independent research projects
• Sustainable by design, empowering our belief that we play an essential role in paving the way to a green society
• Innovative infrastructures and exceptional labs occupying more than 5,000 square meters, including innovations in all that we do
• An environment encouraging curiosity, innovation and entrepreneurship in all areas
• Personalized learning programme to foster our staff’s soft and technical skills
• Multicultural and international work environment with more than 50 nationalities represented in our workforce
• Diverse and inclusive work environment empowering our people to fulfill their personal and professional ambitions
• Gender-friendly environment with multiple actions to attract, develop and retain women in science
• 32 days paid annual leave, 11 public holidays, 13-month salary, statutory health insurance
• Flexible working hours, home working policy and access to lunch vouchers

Temporary contract | 14 + 22 + 12 months| Hautscharage

Are you passionate about research? So are we! Come and join us

How will you contribute?

You will develop a multi-scale numerical simulation framework to assess mechanical cloaking meta-structures within the ANR–FNR Metacloak project (Robust multi-scale design of meta-structures for mechanical cloaking from additive manufacturing processes).

Metacloak targets architected, additively manufactured systems designed to redirect stresses and reduce stress concentrations in critical regions. Mechanical cloaking refers to the ability of a structure to guide and redistribute stresses so that targeted “protected” zones (e.g., around holes, notches, cut-outs, interfaces, or embedded components) experience lower stress peaks. This is particularly relevant for high-demand engineering structures, where local stress concentrations often drive damage initiation and limit service life.

The PhD will focus on a staggered bottom-up modelling workflow, linking micro-scale 3D finite element simulations and numerical homogenisation of metamaterial unit cells to meso-/macro-scale structural models capable of delivering accurate stress fields at a manageable computational cost. You will be exposed to surrogate (AI/ML) approaches to accelerate micro-to-macro links, as well as uncertainty quantification to account for variability in material and geometric parameters.

The objective is to provide a robust virtual assessment capability to compare cloaking concepts against baseline configurations and support the consortium’s broader design activities. The PhD is enrolled at the University of Luxembourg with co-supervision from ENSAM Bordeaux.

Main responsibilities include:

• Perform numerical homogenisation of architected metamaterials (including multi-material configurations) using 3D finite element analyses in dedicated tools (e.g., Abaqus) and extract effective properties.
• Develop and apply hierarchical meso-/macro-scale structural models for anisotropic laminae and laminates (e.g., layer-wise / higher-order plate models) to accurately predict stress fields and assess cloaking performance.
• Build a staggered multi-scale simulation workflow (from micro to laminate/structure), including automation, verification, and clear post-processing metrics for stress concentration reduction.
• Investigate surrogate modelling (AI/ML) to accelerate the micro-to-macro link (e.g., fast prediction of effective properties or response indicators from micro-geometry and material inputs).
• Implement uncertainty quantification (UQ) for material and geometrical/process variability using dedicated tools (e.g., Dakota) and analyse uncertainty propagation on structural responses.
• Document methods and results through reports, figures, conference/journal publications, and contribute to project meetings with the academic partner.

Is Your profile described below? Are you our future colleague? Apply now!

Education

• Master’s degree (or equivalent) in Aerospace Engineering, Mechanical Engineering, Structural Engineering, or a closely related field.
• Candidates from Materials Science, Civil Engineering, Physics, Applied Mathematics, or Computational Engineering are welcome if they demonstrate strong relevant skills.
• Coursework or strong background in computational mechanics / FEM, numerical methods, and scientific programming.
• Exposure to machine learning / data-driven modelling and/or probabilistic methods / uncertainty quantification is an asset.

Experience and skills

• Strong fundamentals in mechanics of solids and structures (stress/strain, elasticity, stability).
• Good understanding of the finite element method (modelling assumptions, boundary conditions, mesh/element choices, convergence checks).
• Experience with at least one FE tool such as Abaqus or similar.
• Scientific programming skills in Python.
• Interest in (or prior exposure to) metamaterials / architected materials (e.g., lattices, TPMS, graded microstructures) and homogenisation concepts.
• Basic understanding of anisotropy and composite materials (lamina properties, laminate stacking, orientation effects) is a plus.
• Familiarity with optimisation and/or surrogate modelling (regression, Gaussian processes, neural nets, etc.) is an asset.
• Interest in uncertainty quantification (sampling methods, sensitivity analysis, propagation of uncertainties) is an asset.
• Ability to work independently and collaboratively, manage tasks, and communicate results clearly (short reports, presentations, well-documented scripts).
• Comfort reading and synthesizing scientific literature and translating it into implementable workflows.

Language skills

• Fluency in English (and French), both oral and written.
• Other relevant languages are an asset.