DIFFER exploits an Ion Beam Facility (IBF) for non-destructive characterization of sub-surface material properties and non-perturbing investigation of processes in materials for fusion energy and solar fuels. Research topics include the investigation of materials exposed to the extreme wall conditions in a nuclear fusion reactor and the structural effects of catalytic materials to accelerate chemical reactions. The facility is built around a 3.5 MV Singletron ion accelerator, in which light ions are extracted from an RF plasma and accelerated electrostatically over (at most) 3.5 MV.
Vacancies
Scientific
Position:
DIFFER is searching for a Tenure Track Group leader that can lead a newly to be formed group on the cross-section between systems and control engineering and energy research specifically between the departments of fusion energy and solar fuels. Hence, the candidate requires a firm background in control engineering
Internship
Modelling non-equilibrium plasmas for CO2 activation is very challenging due to the complex network of chemical reactions and different timescales for the physical and chemical processes involved. An accurate description of electron kinetics is fundamental to calculate chemical rate coefficients and transport parameters that are used to describe the plasma discharge. In the CPPC group, we develop fast and accurate computational approaches for electron kinetics. This MSc project focuses on the application of those approaches to CO2 plasmas investigated experimentally at DIFFER.
Internship
The discovery of new energy materials is becoming a large-scale challenge that is far beyond the reach of experimentation but also stretching the limits of conventional computation. At DIFFER; our AMD research group is working on to improve the speed and the prediction capability of computational methods for the discovery of new energy materials. We use machine learning (ML), which is essentially a method to make predictions and to optimize a performance criterion based on the available example data.
Internship
The computational MSc thesis project is part of a theory-experiment collaboration effort between DIFFER and our industrial partner. The overall aim is to understand the fundamentals of new Fe-based model catalysts, and to tune them further for the Fischer-Tropsch (FT) synthesis of fuels using renewable energy. The fundamental aim is to know how Fe metal layers grow on different Cu metal substrates and how these newly grown Fe layers behave during the adsorption of atomic and molecular species, such as H, C, O, and CO, which are all needed for the synthesis of commercially valuable fuels.
Scientific
We expect applications from senior scientists who wish to lead our SF research programme, establish their own research group within this programme, and join the management team of the institute.
Internship
In a previous Master Thesis project a neural network regression was performed of the warm plasma Ordinary mode dispersion relation. In order to extend this work to the eXtraordinary mode, first a thorough analysis of the different mode branches of the X-mode is required. In particular, around the second harmonic resonance the warm plasma dispersion is characterized by a complex interplay between the fast X-mode and the Bernstein mode, which needs to be documented before a neural network regression can be attempted.
Internship
The 2D reduced MHD code RUTH is used to study the linear and nonlinear evolution of (neoclassical) tearing modes. Within this broad range of topics various thesis projects can be developed ranging from the implementation of more efficient numerical schemes to the implementation of additional physics models and effects such as the ion polarization current, cylindrical coordinates and radial asymmetries and the benchmarking of the effects on the nonlinear growth of the mode against the generalized Rutherford equation.
