This is a joint PhD position at KU Leuven (Belgium) and Silesian University of Technology (Poland). As a PhD student in this project, you will spend the first 2 years at KU Leuven working on multi-physics microstructure evolution simulations and the following 2 years at Silesian University of Technology focusing on attentive generative AI models. Both institutions will be listed on your PhD diploma. You will receive close supervision from both universities and participate in regular project meetings to ensure the continuity of your research.

At KU Leuven, you will be part of the Department of Materials Engineering, specifically in the Nano- and Micro Design of Materials research group (website: https://www.mtm.kuleuven.be/english/research/scalint/NMDM/nano-microstructure-design-materials).

At Silesian University of Technology in Gliwice, you will work at the Faculty of Mechanical Engineering (https://www.polsl.pl/rmt/).

Project

Lithium-ion batteries are considered the future of efficient energy storage. One major issue limiting their lifespan is the formation of lithium dendrites, which can cause short circuits, failures, fires, electrolyte decomposition, and loss of active lithium. Dendrite formation is a complex interfacial process involving multiple length and time scales. Despite extensive research, their composition, structure, and formation remain a significant challenge. To achieve dendrite-free battery interfaces, it is crucial to understand the fundamental mechanisms governing dendritic evolution.

• In this PhD research, you will combine multi-physics finite-element simulations with generative AI models. The multi-physics finite-element model will simulate the morphological evolution of dendrites due to electrochemical reactions, diffusion, convection, and other physical effects in both liquid and solid electrolytes. The resulting computational datasets will be integrated with experimental datasets to train attention mechanism (AM) based generative and/or discriminative machine learning (ML) models, such as MeshGraphNet, variational autoencoders, and transformers.
• The objective is to generate in-silico novel battery architectures or structures that are resistant to dendrite growth.

Profile

We are seeking 2 PhD students with a strong background in engineering, mathematics, materials science, and/or electrochemistry to conduct research on the simulation of dendrite growth in Li-ion battery materials. Programming experience is an advantage.

• As a PhD student, your primary responsibility will be to conduct research in collaboration with other PhD students and researchers. This position involves extensive numerical and computational work, including model development, code implementation, use of finite-element and AI software, and analysis of simulation output. You will also disseminate your research through journal papers, presentations at international conferences, and participation in outreach activities.
• At KU Leuven, you will also be required to attend technical and general subject classes and undertake some teaching duties, such as guiding master's/bachelor's thesis students, assisting in lab and exercise sessions, or supervising exams.
• Admission to the doctoral program at KU Leuven requires a master's diploma with a high score and good English proficiency (IELTS: average of 6.5 or more; TOEFL: average of 79 or more; or equivalent).

For more information on the doctoral program and admission requirements at KU Leuven, visit: https://onderwijsaanbod.kuleuven.be/opleidingen/e/CQ_50045518.htm#activetab=toelatingsvoorwaarden

Offer

• Guidance by world-renowned experts in their field
• International work environment
• Stimulating learning environment

Interested?

For more information, please contact Prof. dr. ir. Nele Moelans at nele.moelans@kuleuven.be, and dr inz. Anil Kunwar at anil.kunwar@plsl.pl.

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