Description:

The group of Kristian Thygesen in the section for Computational Atomic-scale Materials Design (CAMD) at the Technical University of Denmark (DTU), is seeking outstanding and highly motivated candidates for a number of PhD and post doc positions within the area of electronic structure calculations and machine learning with applications to functionalized 2D materials. The positions are funded by the VILLUM Foundation and is part of a newly established Center on Data-Driven Science of 2D Materials.

The successful candidate will develop and apply ab initio-based computational methods to explore different functionalization strategies (e.g. defect engineering, (self-)intercalation, and layer stacking) for enhancing and controlling the fundamental properties of 2D materials.

While the overarching theme of the Center is the exploration of specific 2D functionalization strategies, we have a broad and flexible focus when it comes to the employed methodologies (empirical models, DFT, many-body perturbation theory, machine-learning) the target properties (magnetic, topological, electronic, optical, chemical) and the potential technological applications (opto-electronic devices, electro-catalysis, quantum information). These areas will be covered by several interrelated PhD and post doc projects; the content and goal of this specific post doc project will depend on the interests and qualifications of the candidate.

CAMD (camd) offers an international and scientifically stimulating working environment at the Department of Physics, DTU, located in the northern Copenhagen area. The group has strong connections to local experimental groups within the VILLUM Center for the Science of Sustainable Fuels and Chemicals (http://www.v-sustain.dtu.dk/) and the Center for Nanostructured Graphene (http://www.cng.dtu.dk).

Substantial computational resources are available for the project through the DTU-supercomputer facility Niflheim (https://wiki.fysik.dtu.dk/niflheim/).

To apply, and for further information, see

Postdoc in Machine Learning-Accelerated Modeling and Design of Functionalized 2D Materials

PhD position in Machine-Learning Accelerated Modeling and Design of Functionalized 2D Materials