-
Research Assistant / Research Associate in Frontiers of Atomistic Simulation Techniques (Fixed Term)
interested in the application and methodological development of machine learning techniques to combine electronic structure and modern sampling techniques in liquid-phase heterogeneous catalysis. Experience in
-
of interest in the group, which include: Topological materials Superconductivity Strongly correlated materials Exciton-phonon coupling Machine learning Further details about our areas of interest can be found
-
(healthcare, clinical trials). The research will involve working on research cohort and clinical data, applying machine learning models to synthesise biological (brain imaging, genetic) and cognitive
-
-Dezfooli (Imperial). This is an exceptional opportunity to conduct ambitious research at the forefront of mathematics, statistics, (quantum) information theory, and machine learning. There are generous funds