Sort by
Refine Your Search
-
Employer
-
Field
-
theoretical and numerical modelling project focused on the late stage of terrestrial planet formation involving giant impacts around the Sun and other stars. The project applies state-of-the-art computational
-
international research environment. GIANTS is a five-year theoretical and numerical modelling project focused on the late stage of terrestrial planet formation involving giant impacts around the Sun and other
-
that may threaten the emergence and long-term survival of habitable conditions on orbiting planets. This project places the modelling of cool star high-energy environments at its core, developing a
-
environment. GIANTS is a five-year theoretical and numerical modelling project focused on the late stage of terrestrial planet formation involving giant impacts around the Sun and other stars. The project
-
theoretical models by extending newly derived theoretical frameworks from the ‘OceanCoupling ’ project and numerically implement the theoretical models. We are looking for candidates who can start as soon as
-
to candidates of all nationalities. Our colleague Pedro Duarte wants to hear from potential candidates interested in joining his team to work with coupled physical-biochemical models
-
PhD Research Fellow in Theoretical and Computational Active Matter Physics for Glioblastoma Invasion
consortium and work closely with three other PhD students, combining theory, computation, and experiments to model and manipulate the physical forces experienced by invading cancer cells. The overarching goal
-
system formation modelling to the next level. Several thousands of exoplanets have been discovered in more than 5000 stellar systems, and several thousands more planets are pending confirmation. Although
-
. Existing methods rely on fixed data and static models, which struggle to adapt to real-time changes and unpredictable conditions. This limits the ability to optimize energy storage use for critical
-
the complexity further to effectively plan their movement and deployment. Existing methods rely on fixed data and static models, which struggle to adapt to real-time changes and unpredictable conditions. This