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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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for the generation of clean H2 from non-ideal water streams, looking at oxygen evolution and parallel purification reactions. The candidate(s) will also work with the team to scale-up of the system in area, number
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contracting some products (health insurance, childcare, training, among others.) Training activities: languages, mentoring programme, wellbeing programme. International environment Estimated Incorporation date
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: languages, mentoring programme, wellbeing programme. International environment Estimated Incorporation date: as soon as possible How to apply: All applications must be made via the ICN2 website and include
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: languages, mentoring programme, wellbeing programme. International environment Estimated Incorporation date: as soon as possible How to apply: All applications must be made via the ICN2 website and include
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advantages contracting some products (health insurance, childcare, training, among others.) Training activities: languages, mentoring programme, wellbeing programme. International environment Estimated
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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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physics, which traditionally has relied upon powerful particle colliders, e.g. the LHC at CERN. Axions and axion-like particles (ALPs) are well-motivated dark matter candidates that could solve multiple
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ICFO is offering a postdoctoral position to a well-qualified, highly motivated and dynamic young scientist who wishes to enhance his/her scientific career in a friendly and stimulating environment. The successful candidate will be joining the Optoelectronics group led by Prof. Dr. Valerio...
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analysing deep shotgun metagenomic data in large cohorts, detecting horizontal transmission). We are mostly computational, but have a small lab component and work in close collaboration with the CRG core