9 days ago - req23142
Computational Materials Scientist
Research & development
Chemistry & materials science
In a nutshell
Research & development
Chemistry & materials science
ASML is a leading manufacturer of lithography equipment. In the latest systems, light, plasma and chemicals interact at surfaces. Optimizing processes requires a profound understanding of surface chemistry, plasma-material interactions and of photon induced processes. Since ASML works at cutting edge technology, a lot of phenomena are very unique and have a first-of-a-kind character.
To strengthen the chemistry and materials science competence, ASML Research is looking for a computational materials scientist who can add value to the understanding of surface/materials reactions with the operating environment.
You will work in a multidisciplinary team of Chemists, Physicists, Materials Scientists, Electrical and Mechanical Engineers.
You bring advanced dynamic simulations of complex materials into the big picture by supporting the development of bottom-up modeling workflows which combine low-level and high-level methodologies (e.g. MD, kMC, Coarse-Grain) to bridge time- and length-scales. You will support ongoing efforts in identifying conceptual and methodological gaps required to develop these workflows, and propose innovative solutions to move forward. You have demonstrated experience with modeling of impurities and radiation effects in materials and in particular the impact of impurity diffusion on material properties. Furthermore, you play a key role in implementing and supporting a state-of-the-art infrastructure for materials development.
Based on high-level problem descriptions you define hypotheses on underlying mechanisms governing the behaviors observed from experimental measurements, set up elaborate models to verify the validity of such hypotheses, provide fundamental understanding and translate the results of the simulations into analytic models that can be used to describe the behavior of the system at the material and component level, thereby suggesting design rules to improve product performance.
Your job requires hands-on simulation/modeling of actual systems and problems with required software tools. On the other hand, you have to proactively extend an existing network of commercial and academic partners to a materials modeling ecosystem that guarantees the access of ASML to state-of-the-art developments and techniques in the field.
- University PhD degree in Computational Materials Science / Materials Modeling with a focus on simulation of polycrystalline materials, mass transport and diffusion phenomena in materials, radiation-induced phenomena, and coupling thereof. Affinity to multi-scale methodologies combining discrete and continuum methods gives you a head start. Ideally, you also have experience with topics such as Materials Informatics and Machine Learning.
- Relevant experience of more than 5 years after PhD and broad knowledge of various modeling techniques are essential.
- Demonstrated experience in computational materials science using large-scale particle-based methods for solid-state. Working knowledge in designing and performing simulations to scale-up predictions form low-level atomistic methods, and predict the impact of the microstructure on material behavior. Understanding of equilibrium thermodynamics and equations-of-states. Ideally you are familiar with the theory of activated complexes/transition state theory and chemical kinetics and know how to perform model reduction to condense the complex results of the multiscale simulations into simple rate equations or models useful for the description of experiments in laboratory equipment and machines.
- Broad knowledge in materials behavior and phase transformations and/or chemical kinetics at different time and length scales. Experience in running materials science software tools such as molecular dynamics and monte-carlo methods, and in linking the calculated observables to higher-level numerical (e.g. FEM) and analytical models, the simulations of which are performed using either commercial or in-house software tools.
- Understanding and modeling of mass transport behavior and associated thermomechanical stability of complex materials and composites against external stress factors such as temperature, chemical reactions, grain boundaries, interfaces, etc.
- Knowledge of reactive and diffusion processes in materials and interfaces involving contaminants/impurities and gas phase species including radicals and ions. Basic knowledge on plasma processes is welcome.
- Experience in modeling of H interaction with materials is advantageous.
- Knowledge of machine learning/materials informatics is a plus.
- Candidates with background focused on quantum atomistic methods (e.g. DFT) will not be considered.
- Open to learning techniques outside of the above mentioned fields in the future.
- Proactive and autonomous while driving to success in a highly skilled team of experts towards a common goal.
- Pragmatic attitude with an analytical view.
- Able to see the big picture and create a vision and have perseverance in realizing it.
Context of the position
The Research Materials Science andChemistry group is part of the Research department of ASML. This group identifies and fills in technological gaps in the future roadmap of lithography and the lithography market. Our focus is on advanced material research and on the interaction of light (13.5 nm) with the gaseous background in our tools and on solid materials. We work in small teams and deliver solutions that can be transferred to Development and Engineering. We work together with external research institutes and universities.
This position may require access to controlled technology, as defined in the Export Administration Regulations (15 C.F.R. §730, et seq.). Qualified candidates must be legally authorized to access such controlled technology prior to beginning work. Business demands may require ASML to proceed with candidates who are immediately eligible to access controlled technology.
Keywords: Materials science, Hierarchical and Concurrent Multiscale Modeling, Molecular Dynamics, Coarse-Graining, Kinetic Monte Carlo, Kinetics, Thermodynamics, Phase Diagrams, Machine learning.
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