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of PhDs, and undergraduate students (thesis defence, students follow up, etc) · Tracking of the activities of the AEMD Group (team members, thesis, publications, group activities, patents, and
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benchmarking. Contribution to SIESTA training events. Contribution to other activities in the group. Requirements: PhD in Physics, Materials Science, Chemistry, Computer Science, or related disciplines
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internal reports and manuscripts. Requirements: PhD in Physics, Materials Science, Computational Science/Engineering, Computer Science, or related. Solid knowledge of machine learning, including graph neural
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. Requirements: Minimum: PhD in Physics, Materials Science, Computational Science/Engineering, Computer Science, or related field. Demonstrated experience implementing heuristic/metaheuristic optimisation (e.g
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. Requirements: Minimum: PhD in Physics, Materials Science, Computational Science/Engineering, Computer Science, or related field. Demonstrated experience implementing heuristic/metaheuristic optimisation (e.g
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internal reports and manuscripts. Requirements: PhD in Physics, Materials Science, Computational Science/Engineering, Computer Science, or related. Solid knowledge of machine learning, including graph neural
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of the tribometer include the following tasks: Design automated load selection synchronized with mechanical stimulus. Design current and voltage sensing for power generation characterization. Model time response
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students and other researchers on in-situ (S)TEM. Requirements: · Education: PhD in Chemistry, Physics or Material Science, or closely related fields, with a strong focus on nanomaterials and advanced
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papers. Requirements: Education: PhD in Physics or related degree Knowledge: Complex oxides (Ferroelectrics, antiferroelectric, nickelates, etc), nanomechanics, photovoltaics, scanning force microscopy
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to) SIESTA (www.siesta-project.org) and its TranSIESTA functionality. SIESTA is a multi-purpose first-principles method and program, based on Density Functional Theory, which can be used to describe the atomic