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Position: PhD scholarship in discovery of Kagome materials for magnetic catalysis
Institution: Technical University of Denmark
Location: Kongens Lyngby, Lyngby‐Taarbæk Municipality, Denmark
Duties: The successful candidate will break new ground in understanding of the complex multiphysics phenomena happening at the electrocatalytic interface under a magnetic field. The candidate will use AI-accelerated atomic-scale simulations, mainly in the framework of density functional theory, ab-initio molecular dynamics, and machine learning-classical potentials, to study the electrochemical response to external magnetic moments in Kagome Materials. The goal is to discover materials and tunable magnetic conditions able to reduce reaction overpotentials and increase the electrochemical activity for the oxygen evolution and reduction reactions. The successful candidate will contribute to the development of autonomous workflows for interfaces and to develop a data infrastructure
Requirements: You must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree in physics, chemistry or engineering; has a proven expertise in computational/theoretical physics, chemistry, materials science and related areas; has proven expertise in materials modelling is required
   
Text: PhD scholarship in discovery of Kagome materials for magnetic catalysis The successful candidate will break new ground in understanding of the complex multiphysics phenomena happening at the electrocatalytic interface under a magnetic field. The candidate will use AI-accelerated atomic-scale simulations, mainly in the framework of density functional theory, ab-initio molecular dynamics, and machine learning-classical potentials, to study the electrochemical response to external magnetic moments in Kagome Materials. The goal is to discover materials and tunable magnetic conditions able to reduce reaction overpotentials and increase the electrochemical activity for the oxygen evolution and reduction reactions. The successful candidate will contribute to the development of autonomous workflows for interfaces and to develop a data infrastructure You must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree in physics, chemistry or engineering; has a proven expertise in computational/theoretical physics, chemistry, materials science and related areas; has proven expertise in materials modelling is required
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