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novel energy harvesting devices for the development of self-driven neural interfaces. Requirements: PhD in Energy, Electrochemistry, Materials Science, Nanotechnology, or equivalent degrees. Knowledge and
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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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such as ATAC-seq, chIP-seq and 3D chromatin conformation capture data You have experience in the analysis of single-cell data Education and training You hold a PhD degree in Computational Biology Languages
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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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PrecisionTox. Our aim is to employ existing and develop novel computational approaches to assess the impact of the chemical compounds on gene expression or co-expression networks across the model species and
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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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, and abroad. Required qualifications: The candidate should have a PhD in condensed matter physics, with strong background in high vacuum, cryogenics, electronics, transport and magnetic measurements
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computers and, as a result, many of them remain poorly understood. UItracold atoms trapped in optical lattices provide a pristine realization of the Hubbard model and hold the promise of solving many of its
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PhD students working in quantum technologies at ICFO and with strong collaborations with our European academic partners. This project is funded by MCIN with funding from European Union NextGenerationEU