Postdoctoral Researchers – Multiphysics Modelling of PVD-Functionalized Battery Electrodes

The research landscape at the University of Namur currently comprises 11 Institutes, bringing together complementary expertise around major scientific themes and fostering transdisciplinary collaborations as well as innovative approaches to research and teaching.

Within this ecosystem, the Namur Institute of Structured Matter (NISM) is a leading interdisciplinary research institute where the successful candidate will be based. NISM's research interests cover diverse topics in organic and physical chemistry, materials chemistry, surface science, solid-state chemistry and physics, from both theoretical and experimental perspectives.

We are looking for highly motivated Postdoctoral Researchers in Battery Modelling and Multiphysics Simulation to develop a modelling framework linking:

PVD process → thin-film morphology → electrode microstructure → electrochemical behaviour → full-cell performance

The two successful candidates will investigate how the morphology and physical properties predicted by Virtual-Coater™ can be translated into meaningful input parameters for battery modelling platforms. The ultimate objective is to establish a predictive modelling workflow capable of assessing how PVD functionalization influences battery performance, including:

• capacity;
• charge/discharge behaviour;
• rate capability;
• ionic and electronic transport;
• polarization and overpotential;
• energy efficiency;
• cycle behaviour and degradation;
• influence of electrode architecture and coating morphology.

A particular objective will be to identify the appropriate level of model complexity required to represent PVD-functionalized electrodes without unnecessarily increasing computational cost.

The work will combine fundamental modelling, numerical simulation and experimental validation, with a strong emphasis on technology transfer to industrial partners.

We are looking for two researchers with PhD in one of the following fields:

• electrochemical engineering;
• chemical engineering;
• materials science;
• battery science;
• computational materials science;
• applied physics;
• mechanical engineering;
• energy systems;
• computational science;
• or a closely related discipline.

Essential skills

The candidates should have strong experience in several of the following areas:

• electrochemical modelling;
• lithium-ion or other rechargeable battery systems;
• porous-electrode theory;
• electrochemical transport phenomena;
• numerical modelling and simulation;
• multiphysics modelling;
• Python or c++ and scientific computing.

Experience with PyBaMM, PyBattMo, COMSOL, FEniCS , or comparable simulation tools is highly desirable.

A good understanding of battery electrode architecture, including porosity, tortuosity, effective transport properties, interfaces and surface coatings, would be particularly valuable.

Valuable additional experience

Experience in one or more of the following areas would be considered an advantage:

• plasma surface functionalization;
• electrode/electrolyte interfaces;
• battery degradation modelling;
• microstructure-resolved modelling;
• tomography or image-based electrode modelling;
• machine learning/data-driven modelling;
• parameter identification and optimization;
• scientific software development.

Importantly, direct experience with PVD is not mandatory. Candidates with strong backgrounds in electrochemical or battery modelling who are interested in developing expertise in plasma-based materials processing are encouraged to apply.

The postdoctoral researcher is expected to contribute to:

1. A validated methodology for translating PVD thin-film morphology into battery-model parameters.
2. New or adapted electrochemical models describing PVD-functionalized electrodes.
3. Simulation tools based on PyBaMM, PyBattMo and/or complementary modelling platforms.
4. Quantitative understanding of the relationship between coating morphology and battery performance.
5. Experimental validation of the modelling framework.
6. A digital workflow connecting PVD process parameters to predicted battery performance.
7. Scientific publications and conference contributions.
8. A demonstrable modelling tool supporting industrial technology transfer.