In these projects, the student will use atomistic simulation
techniques such as Density Functional Theory and high performance
computing to study transport or adsorption phenomena. One project
would focus on investigating diffusion of Li in rutile (TiO₂), while
another would explore the binding energies of molecules to strained
metal surfaces for catalytic applications. Both projects are motivated
by experiments performed in SBQMI labs. A third area of interest is
the use of machine learning techniques to describe efficiently the
interaction between atoms in complex alloys and compounds with quantum
mechanical accuracy. Candidates should have knowledge of solid state
physics, good computer literacy and some programming experience
(python, C++, etc.).