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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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Research Engineer - Tools developer for LSQUANT platform (Theoretical and Computational Nanoscience)
different areas of nanoscience and nanotechnology. Job Title: Research Engineer - Tools developer for LSQUANT platform Research area or group: Theoretical and Computational Nanoscience Group Description
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: Education: ·PhD in chemistry, biochemistry. Master in similar fields will be positivitely valorated. Knowledge and experience: ·Background in biosensor devices and clinical applications ·Knowledge in
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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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, with a highly innovative research and technology transfer project, this could be your opportunity. Main Tasks and responsibilities: To develop and optimize the analytical performances of the multipled
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multiple Research Support Units and Facilities, staffed by highly skilled scientists and technicians whose diverse expertise adds substantial value to ICN2's research community. The division has experienced
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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