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highly desirable. Knowledge on AI ML/DL coding applied to STEM / EELS data analysis automation is also needed. · Professional Experience: Previous research experience in an academic or industrial setting
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properties of high-entropy alloy (HEA) nanoparticles. The SIESTA code will be used to compute key descriptors such as formation energies, mixing enthalpies, adsorption energies, d-band centers, and work
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to developing an HR Strategy for Researchers, designed to bring the practices and procedures in line with the principles of the European Charter for Researchers and the Code of Conduct for the Recruitment
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: MSc in Physics, Materials Science, Nanoscience, Computer Engineering, Data Science, Gaming Engineering or a related discipline. · Knowledge: Strong coding skills in Python and knowledge in materials
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widely used by the academic community (about one thousand citations per year), and has been a flagship code of the MaX European Centre of Excellence for exascale computing in Materials Science (www.max
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Experience with optical simulation software used in gravitational-waves like Finesse, SIS, and others; knowledge of commercial codes like Zemax are a plus. Experience with electronics or hardware development
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Experience developing pipelines and code for gravitational-wave searches and/or parameter estimation Knowledge of advanced Bayesian methods and samplers, machine learning approaches to signal processing
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of Group/Project: We are building an optimisation-driven framework that makes AI models reliably operate advanced scientific software (e.g., DFT, Wannierisation, and quantum-transport codes) and (ii) uses