Doctoral researcher (PhD student) in area-selective atomic layer deposition (AS-ALD) simulations

About the role

Aalto University is where science and art meet technology and business. We shape a sustainable future by making research breakthroughs in and across our disciplines, sparking the game changers of tomorrow and creating novel solutions to major global challenges. Our community is made up of 16 000 students and 5 200 employees, including 446 professors . Our campus is in Espoo, Greater Helsinki, Finland. Diversity is part of who we are, and we actively work to ensure our community's diversity and inclusiveness. This is why we warmly encourage qualified candidates from all backgrounds to join our community. The School of Chemical Engineering ( CHEM School ) is one of the six schools of Aalto University. It combines natural sciences and engineering in a unique way. The Department of Chemistry and Materials Science (CMAT) is looking for: Doctoral researcher (PhD student) in area-selective atomic layer deposition (AS-ALD) simulations Are you interested in discovering and understanding chemical reactivity on surfaces, with direct industrial and societal relevance? Do you enjoy research, and does the prospect of using simulations and theory to inform and guide experiments motivate you? We are now seeking a doctoral researcher possessing a curious and self-driven mindset to develop and apply new simulation protocols to push towards realistic and dynamic modeling of AS-ALD processes. Recently, we have made significant progress in this direction by merging machine learning interatomic potentials (MLIPs) trained on density functional theory (DFT) data, and enhanced sampling techniques to reach the necessary time and size scales, enabling realistic thin-film growth simulations with near-quantum accuracy at a fraction of the computational cost. The tools developed in this position have the potential to have an tangible impact on the development of improved AS-ALD processes for semiconductor manufacturing. Your role and goals The main focus of your research will be to unravel the mechanisms that provide selectivity in AS-ALD, as well as the factors leading to degradation and loss of selectivity. To achieve this, it is critical to establish a reliable methodology for training and validating of underlying MLIPs, including testing these against the reference method (DFT) for properties such as adsorption, diffusion and reaction energies. Furthermore, we aim to answer the following questions: How can various small-molecule inhibitors in combination with different non-growth and growth areas be exploited in the design of new AS-ALD processes? How do the surfaces and deposited thin-films evolve during typical ALD operating conditions? What are the main mechanisms behind area selectivity in superlattice-based AS-ALD (SAS-ALD)? Are there any non-established ways to induce area-selectivity? The goal is for you to become an expert in ALD simulations, large-scale reaction modelling and enhanced sampling methods. This implies that you will command both gas-phase and interfacial dynamics, and the skills you acquire are therefore highly transferable and applicable to related fields such as heterogeneous catalysis, as well as material and surface sciences in general. You will have the opportunity to share your results with and learn from an interational research community at conferences and during mobility. Your network and team The CMAT Department offers a multi-disciplinary working environment, focused on micro-, nano-, and atomic scale engineering of compounds and materials. You will work in the Academy Research Fellowship project " SAMPALE: Towards realistic atomic layer deposition reaction models aided by enhanced sampling and machine learning " of Dr. Joakim Jestilä, who will be your doctoral advisor and the principal investigator (PI). You will be part of the Inorganic Materials Modelling group (led by Prof. Antti Karttunen). On the experimental side, the team is working closely with the Inorganic Materials Chemistry (headed by Prof. Maarit Karppinen) and the Atomically Controlled Materials Engineering (ACME) groups (headed by Prof. Ville Miikkulainen), both housing leading ALD-expertise, offering ample opportunities for collaboration and experimental testing of promising results in-house. Your experience and ambitions We encourage you to apply if you have: A Master's degree in Chemistry, (Chemical) Physics, or a related field Experience with reaction modelling (minimum energy pathways, transition states, reaction kinetics), especially for reactions on surfaces Hands-on experience with high-performance computing (HPC) environments, coding/scripting (Python, Bash), and version control (Git) Strong command of written and spoken English Finnish or Scandinavian language (SWE, NO, DK) proficiency is considered an advantage Furthermore, it is considered highly advantageous if you have experience in any of the following: ALD, training and fine-tuning of MLIPs, foundation models, enhanced sampling techniques such as metad

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