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PhD position: Machine Learning Crystal Defects f ... (No replies)
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Would you like to combine cutting edge machine learning models with electronic structure calculations to explore and discover new solid state platforms for quantum technologies?
The section for Computational Atomic-scale Materials Design (CAMD) at the Technical University of Denmark (DTU), is seeking an outstanding and highly motivated candidate for a PhD position within the area of high-throughput electronic structure calculations and machine learning of quantum point defects in crystalline solids. The project will be supervised by Prof. Kristian Thygesen, and is part of the recently established EU Marie Curie training network EUSpecLab – a collaboration between 23 universities and companies – that funds several PhD projects under the common theme of machine learning for (theoretical) spectroscopy.
As a PhD student within the EUSpecLab network, you will employ ab-initio methods to explore the electronic, optical, and magnetic properties of crystalline point defects with the goal of identifying novel systems for quantum technological applications such as single-photon light sources, quantum magnetic field sensors, and spin qubits. As part of the project, you will design and implement automated workflows for high-throughput characterization of defects and integrate them with machine learning algorithms to guide and accelerate the search for better defect systems. You will also be developing machine learning models to predict spectroscopic properties of crystal defects and thereby circumvent costly ab-initio calculations.
CAMD offers an international and scientifically stimulating working environment at the Department of Physics, DTU, located in the northern Copenhagen area. The section develops the GPAW electronic structure code, the Atomic Simulation Environment (ASE) as well as other open source software projects and databases. Substantial computational resources are available for the project through the DTU-supercomputer facility Niflheim.
To read more and apply, please follow this link: PhD position: Machine Learning Crystal Defects for Quantum Technology (dtu.dk)