About the position:
We are pleased to offer a PhD opportunity in the field of computational
materials science, with a focus on pioneering research in metal oxide
growth and dynamics by hybrid molecular beam epitaxy (H-MBE).
These materials are vital across multiple industrial sectors, including catalysis,
electronics, energy storage, and optical technologies, due to their versatile
and valuable properties. Mastery over these properties is crucial for advancing
performance and functionality in these applications. H-MBE presents significant
benefits over traditional MBE by enabling the high-quality growth of metal
oxides with exceptional control over both composition and structure.
Unlike conventional MBE, which typically involves simpler precursor systems,
H-MBE incorporates additional complexities such as organic precursors and
tailored growth conditions designed to optimize material properties.
Understanding these complexities requires a deep dive into the atomistic
mechanisms at play, where computational models are indispensable for
providing insights that are difficult to capture experimentally.
materials science, with a focus on pioneering research in metal oxide
growth and dynamics by hybrid molecular beam epitaxy (H-MBE).
These materials are vital across multiple industrial sectors, including catalysis,
electronics, energy storage, and optical technologies, due to their versatile
and valuable properties. Mastery over these properties is crucial for advancing
performance and functionality in these applications. H-MBE presents significant
benefits over traditional MBE by enabling the high-quality growth of metal
oxides with exceptional control over both composition and structure.
Unlike conventional MBE, which typically involves simpler precursor systems,
H-MBE incorporates additional complexities such as organic precursors and
tailored growth conditions designed to optimize material properties.
Understanding these complexities requires a deep dive into the atomistic
mechanisms at play, where computational models are indispensable for
providing insights that are difficult to capture experimentally.