PhD candidate || Modelling plasma surface interactions with machine learning
- Omvang (fte)
- Geplaatst op
- 24 mei 2022
- 1 juli 2022 Nog 4 dagen om te solliciteren
The Faculty of Science and the Leiden Institute of Chemistry are looking for a:
PhD candidate || Modelling plasma surface interactions with machine learning (1.0 fte)
Vacancy number: 22-331
Plasma surface interactions (PSI) are of relevance in many industrial applications. PSI-models currently used in engineering are empirically motivated and parameterized based on prototypical lab experiments. Consequently, they lack important chemical insights at the atomic scale as well as transferability. In principle, state-of-the-art electronic structure calculations based on density functional theory (DFT) are able to accurately describe all the aforementioned atomic-scale phenomena in a material-specific fashion. In practice, however, their high computational cost limits such calculations to very small length and time scales (nano meters and nano seconds, respectively). Coarse-graining the effect of the electrons on the chemical interactions into simplified interatomic potentials based on analytical expressions with a plethora of material-specific parameters allows to reduce the computational effort, but can also significantly limit the accuracy – in particular when it comes to describing the making and breaking of chemical bonds. Machine-learning-based (ML-based) interatomic potentials have been demonstrated to overcome these limitations for particular systems and thus scale-up DFT-quality models to length and time scale that are interesting for modeling PSI. The project aims at bridging the gap towards applications in an industrial setting and is carried out in close collaboration with ASML.
- Benchmark of computationally (more) efficient ML potentials for reaction dynamics of N2/Ru(0001) against an accurate reference from the Leiden group (K. Shakouri, et al., J. Phys. Chem. Lett. 8, 2131 (2017), DOI: 10.1021/acs.jpclett.7b00784);
- Extension to higher impact energies of N and N2 projectiles;
- Atomic scale analysis of concomitant energy transfer and substrate damage.
- MSc degree in Physics or Chemistry with focus on and passion for computational modeling and machine learning;
- Excellent proficiency in English;
- Experiences with Linux and numerical simulations;
- Scripting for the automatization of calculations and their analysis (preferentially with Python);
- Other desirable skills: further development programming skills, experiences with large-scale computing facilities, DFT calculations for periodic systems (VASP etc.), Atomic Simulation Environment (ASE).
The Faculty of Science is a world-class faculty where staff and students work together in a dynamic international environment. It is a faculty where personal and academic development is top priorities. Our people are committed to expand fundamental knowledge by curiosity and to look beyond the borders of their own discipline; their aim is to benefit science, and to make a contribution to addressing the major societal challenges of the future.
The research carried out at the Faculty of Science is very diverse, ranging from mathematics, information science, astronomy, physics, chemistry and bio-pharmaceutical sciences to biology and environmental sciences. The research activities are organized in eight institutes. These institutes offer eight bachelor’s and twelve master’s programs. The faculty has grown strongly in recent years and now has more than 2,300 staff and almost 5,000 students. We are located at the heart of Leiden’s Bio Science Park, one of Europe’s biggest science parks, where university and business life come together.
The chemistry and life science research in the Leiden Institute of Chemistry (LIC) is organized around two major research areas: ‘Chemical Biology’ and ‘Energy & Sustainability’. The institute’s research themes illustrate the central position of chemistry between biology, medicine and physics. The various research topics carried out within these themes are ideal for executing interdisciplinary research.
Terms and conditions
We offer a full-time position for initially one year. After positive evaluation of the progress of the thesis, personal capabilities and compatibility the appointment will be extended by a further three years. Salary range from € 2.443 to € 3.122 gross per month (pay scale P in accordance with the Collective Labour Agreement for Dutch Universities).
Leiden University offers an attractive benefits package with additional holiday (8%) and end-of-year bonuses (8.3 %), training and career development and sabbatical leave. Our individual choices model gives you some freedom to assemble your own set of terms and conditions. Candidates from outside the Netherlands may be eligible for a substantial tax break.
All our PhD students are embedded in the Leiden University Graduate School of Science. Our graduate school offers several PhD training courses at three levels: professional courses, skills training and personal effectiveness.
Diversity and inclusion are core values of Leiden University. Leiden University is committed to becoming an inclusive community which enables all students and staff to feel valued and respected and to develop their full potential. Diversity in experiences and perspectives enriches our teaching and strengthens our research. High quality teaching and research is inclusive.
If you have any questions regarding the application procedure, please contact Mrs. van der Haar, firstname.lastname@example.org (secretary of the Theoretical Chemistry group). Enquiries related to the project can be made to Dr. Jörg Meyer, email@example.com.
Please send your application to Mrs. van der Haar firstname.lastname@example.org including the following documents:
- A letter of motivation;
- An updated CV;
- Copy of master’s thesis or any other publication (if available);
- The transcripts of your MSc studies and copy of your degree;
- Written letters of recommendation by at least two former supervisors.
Application deadline is July 1 th. Applications will be considered on a rolling basis