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to) SIESTA (www.siesta-project.org) and its TranSIESTA functionality. SIESTA is a multipurpose first-principles method and program, based on Density Functional Theory, which can be used to describe the atomic
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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
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areas of nanoscience and nanotechnology Job Title: Research Engineer - AI Development for Materials Design Research area or group: Theoretical and Computational Nanoscience Group Description of Group
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., evolutionary algorithms/strategies, mixed-integer search, multi-objective methods). Strong Python and scientific-computing skills (data handling, experiment tracking, testing, version control). Practical
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., evolutionary algorithms/strategies, mixed-integer search, multi-objective methods). Strong Python and scientific-computing skills (data handling, experiment tracking, testing, version control). Practical
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surfaces. -Structural and biophysical characterization: Exhaustive characterization of the coatings by advanced methods (FTIR, UV-Vis, electron microscopies, TGA, NMR/EPR spectroscopy for structural
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methods with in situ characterisation techniques such as in situ Raman spectroscopy (including SHINERS) and electrochemical mass spectrometry. This combination will be used to investigate the structure
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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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characterization techniques. · Knowledge: Deep expertise in electron microscopy, particularly STEM and FIB methods. Proven experience in designing and conducting in-situ TEM experiments. Familiarity with energy
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synthesis methods. Familiarity with microscopy image processing or quantitative analysis tools (e.g., ImageJ, Python-based analysis) will be valued. Motivation to work at the intersection of experimental