Doctoral researcher (PhD student) in area-selective atomic layer deposition (AS-ALD) simulations
Core
Develop and apply new simulation protocols to model area-selective atomic layer deposition (AS-ALD) processes, focusing on unraveling mechanisms of selectivity and degradation using machine learning interatomic potentials (MLIPs) and enhanced sampling techniques.
Role type
Doctoral researcher (PhD student) in computational materials science and surface chemistry
Builds
Realistic and dynamic modeling of AS-ALD processes for semiconductor manufacturing
Domain
Chemical engineering, materials science, surface chemistry, semiconductor manufacturing
Deliverable
production ML models
Required skills
reaction modelling (minimum energy pathways, transition states, reaction kinetics), high-performance computing (HPC) environments, coding/scripting (Python, Bash), version control (Git), gas-phase and interfacial dynamics
Preferred skills
ALD processes, training and fine-tuning of MLIPs, foundation models, enhanced sampling techniques (metadynamics), molecular dynamics
Technologies
DFT, MLIPs, metadynamics, molecular dynamics, LUMI supercomputer
Responsibilities
Establish reliable methodology for training and validating MLIPs against DFT reference methods; investigate mechanisms of area selectivity and degradation in AS-ALD; model surface and thin-film evolution under operating conditions; explore non-established ways to induce area-selectivity
Seniority
Junior, research-focused IC