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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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. 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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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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: · 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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institution and they are allocated through recurrent internal calls). Preparation of scientific reports, journal articles and software documentation. Requirements: PhD in Physics, Materials Science, Chemistry
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. Preparation of scientific manuscripts and presentations in workshops or conferences to showcase your research results to the scientific community. Skills on proposal writing. Requirements: PhD degree in
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applications. The candidate will help on MSc and PhD students supervision and training. AVAILABLE INSTRUMENTS Thermo Fisher Spectra 300 (60-300 keV), double corrected and monochromated, Gatan Continuum EELS with