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areas of nanoscience and nanotechnology. Job Title: Research Assistant - AI Optimisation for Materials Design Research area or group: Theoretical and Computational Nanoscience Group Description of Group
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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
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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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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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. The candidate will provide guidance and support to MSc and PhD students, assisting in the supervision and training of junior researchers in areas such as device fabrication, biosensor assembly, and
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meeting and participating to annual project meetings. The candidate will provide guidance and support to MSc and PhD students, assisting in the supervision and training of junior researchers in areas
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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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: · Education: PhD in Materials Science or similar. Knowledge in tech transfer will be highly valuable. · Knowledge: Advanced materials development Polymeric materials development, functionalization
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: · Education: PhD in Materials Science or similar. Knowledge in tech transfer will be highly valuable. · Knowledge: Advanced materials development Polymeric materials development, functionalization
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areas of nanoscience and nanotechnology. Job Title: Research Assistant in Active learning Research area or group: Theoretical and Computational Nanoscience Group Description of Group/Project: In