PhD Position (Embrittlement Cutting of Structural Steel and Alloys)
We are looking for a PhD student which is fully funded for EU/UK student.
We are looking for a PhD student which is fully funded for EU/UK student.
The Division of Mechanics at Lund University, Sweden, invites highly motivated and creative applicants for a PhD position oriented towards multiscale modelling of yielding and failure of materials subjected to harsh conditions in energy applications (e.g. components in fusion reactors, battery components, etc.). The research program focuses on improving the understanding of the yielding and failure mechanisms, with the ultimate goal to develop atomistically-informed continuum-based theoretical and computational models to predict yielding and failure in energy materials.
The group on “Mechanics of soft and living interfaces” (https://www.lacan.upc.edu/mechanics-of-soft-and-living-interfaces/) lead by Marino Arroyo (https://www.lacan.upc.edu/arroyo/) is looking for a highly motivated and creative postdoctoral researcher to study the mechanical organization of epithelial cells and tissues, and how this understanding can lead to a precise control of tissue structure, mechanical properties, and dynamics.
We've recently published our new study about Uncertainty Quantification in Molecular Dynamics (MD) Simulations. Due to the selection of functional forms of interatomic potentials or the numerical approximation, MD simulations may predict different material behavior from experiments or other high-fidelity results. In this study, we used Stochastic Reduced Order Modeling (SROM) to achieve
(1) mechanical behavior of graphene predicted by MD simulations in good agreement with the continuum model which has been calibrated by experiments;
Composite materials are increasingly used in industry due to the possibility of tailoring their properties based on the applications. Their greatest advantage is strength and stiffness combined with lightness. However, their optimal design and performance is still limited by the lack of knowledge of physical mechanisms that control their fracture behavior. Machine learning and big data driven approaches have not been extensively studied for fracture behavior predictions.
The focus of this symposium is to discuss current research and key developments in theory, computational and experimental methods to study and predict the mechanical properties of materials in application-orientated environments. These environments may include, but are not limited to high temperature, cryogenic temperature, electrical and magnetic field, gas, radiation, chemical, pressure extremes, and humidity.
Many of modern life activities involve the risk of fire, explosions, and impacts. In addition, natural extreme events are becoming more and more common. us, robustness, the ability to avoid disproportionate collapse due to an initial damage, and resilience, the ability to adapt to and recover from the effects of changing external conditions, represent two important characteristics of current structures and infrastructures. eir definitions are reviewed in this paper with the aim of sorting and describing the different approaches proposed in the literature and in the international standards.